Per METplus Discussion dtcenter/METplus#2063 from @RogerHar, the igno…

…rance score equation in Appendix C is slightly incorrect. Updating the main_v11.0 branch to make it consistent with equation 8 in Gneiting et al 2005.

docs/Users_Guide/appendixC.rst

@@ -1101,7 +1101,7 @@ Called "IGN" in ECNT output :numref:`table_ES_header_info_es_out_ECNT`
 
 The ignorance score (IGN) is the negative logarithm of a predictive probability density function (:ref:`Gneiting et al., 2004 <Gneiting-2004>`). In MET, the IGN is calculated based on a normal approximation to the forecast distribution (i.e. a normal pdf is fit to the forecast values). This approximation may not be valid, especially for discontinuous forecasts like precipitation, and also for very skewed forecasts. For a single normal distribution **N** with parameters :math:`\mu \text{ and } \sigma`, the ignorance score is
 
-.. math:: \text{ign} (N( \mu, \sigma),y) = \frac{1}{2} \ln (2 \pi \sigma^2 ) + \frac{(y - \mu)^2}{\sigma^2}.
+.. math:: \text{ign} (N( \mu, \sigma),y) = \frac{1}{2} \ln (2 \pi \sigma^2 ) + \frac{(y - \mu)^2}{2\sigma^2}.
 
 Accumulation of the ignorance score for many forecasts is via the average of individual ignorance scores. This average ignorance score is the value output by the MET software. Like many error statistics, the IGN is negatively oriented, so smaller numbers indicate better forecasts.