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Stream 1 - Software development for weather, climate and atmosphere
Goal
This project is a follow-up of the ESoWC 2020 data encoding optimisation challenge.
Based on the results and the findings of the completed project we will implement improved data packing configuration in our production streams. We would also like to analyze some new atmospheric composition and meteorological datasets.
Some knowledge of meteorological data formats (GRIB, NetCDF) and libraries to decode and manipulate them (ecCodes, netcdf, cdo, nco, ..)
Some knowledge about data encoding (data packing, accuracy, compression methods)
Knowledge of a software library to compute and present the results
Some familiarity with Chemical Transport Modelling (CTM) or Numerical Weather Prediction (NWP) to be able to better appreciate this challenge would be beneficial
Note: Challenge is funded by Copernicus. Only nationals from the European Union and ECMWF Member States are eligible to apply (see Terms and Conditions).
Challenge description
Data and software
We plan to use the CAMS global real-time forecast dataset, ecCodes and NetCDF libraries to test different configurations and estimate data encoding errors and software library to compute and present results (Python, R or Julia).
What is the current problem?
Due to non-optimal data encoding configuration, there is a lot of artificial precision in our data. Datasets are expensive to archive and move and difficult to use.
What could be the solution?
We would like to remove artificial precision from the encoded fields without any loss of information. At the same time, we need to be conscious of operational constraints, so data encoding and decoding steps do not become prohibitively expensive. The desired solution would be a combination of data encoding settings and step to achieve this goal.
Ideas for the implementation
Things to address: more appropriate packing methods, encoding float arrays, explore usage of suitable data compression algorithms.
The text was updated successfully, but these errors were encountered:
jwagemann
changed the title
Challenge #31 - Jupyter widgets to help process and explore meteorological data
Challenge #12- Size, precision, speed - pick two : implementation
Jan 28, 2021
EsperanzaCuartero
changed the title
Challenge #12- Size, precision, speed - pick two : implementation
Challenge #12- Size, precision, speed - pick two: implementation
Jan 29, 2021
Hi,
join us for the ECMWF Summer of Weather Code Ask Me Anything session and learn all things ESoWC.
When: Wednesday, 24 March 2021 at 4 pm GMT
What: learn everything about ESoWC - how it works, the challenges this year, some tips for your proposal and listen to ESoWC experiences from previous participants
Challenge 12 - Size, precision, speed - pick two: implementation
Goal
This project is a follow-up of the ESoWC 2020 data encoding optimisation challenge.
Based on the results and the findings of the completed project we will implement improved data packing configuration in our production streams. We would also like to analyze some new atmospheric composition and meteorological datasets.
Mentors and skills
Challenge description
Data and software
We plan to use the CAMS global real-time forecast dataset, ecCodes and NetCDF libraries to test different configurations and estimate data encoding errors and software library to compute and present results (Python, R or Julia).
What is the current problem?
Due to non-optimal data encoding configuration, there is a lot of artificial precision in our data. Datasets are expensive to archive and move and difficult to use.
What could be the solution?
We would like to remove artificial precision from the encoded fields without any loss of information. At the same time, we need to be conscious of operational constraints, so data encoding and decoding steps do not become prohibitively expensive. The desired solution would be a combination of data encoding settings and step to achieve this goal.
Ideas for the implementation
Things to address: more appropriate packing methods, encoding float arrays, explore usage of suitable data compression algorithms.
The text was updated successfully, but these errors were encountered: