Update main_v11.0-ref after #2494

.readthedocs.yaml

@@ -7,7 +7,7 @@ version: 2
 
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 #formats: all
-#formats: [pdf]
+formats: [pdf]
 
 # Optionally set the version of Python and requirements required to build your
 # docs

LICENSE.md

@@ -0,0 +1,174 @@
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docs/Users_Guide/appendixC.rst

@@ -765,17 +765,18 @@ where the weights are applied at each grid location, with values assigned accord
 
 .. only:: latex
 
-  .. math::  
+  .. math::
 
     \text{S1\_OG} = \frac{\text{EGBAR}}{\text{OGBAR}}
 
     \text{FGOG\_RATIO} = \frac{\text{FGBAR}}{\text{OGBAR}}
 
 .. only:: html
 
-  .. math::  
+  .. math::
+
     \text{S1_OG} = \frac{\text{EGBAR}}{\text{OGBAR}}
-    
+
     \text{FGOG_RATIO} = \frac{\text{FGBAR}}{\text{OGBAR}}
 
 
@@ -989,7 +990,7 @@ Let :math:`\text{J}` be the number of categories, then both the forecast, :math:
 :math:`F_m = \sum_{j=1}^m (f_j)` and :math:`O_m = \sum_{j=1}^m (o_j), m = 1,…,J`.
 
 
-To clarify, :math:`F_1 = f_1` is the first component of :math:`F_m`, :math:`F_2 = f_1+f_2`, etc., and :math:`F_J = 1`.  Similarly, if :math:`o_j = 1` and :math:`i < j`, then :math:`O_i = 0` and when :math:`i≥j`, :math:`O_i = 1`, and of course, :math:`O_J = 1`.  Finally, the RPS is defined to be:
+To clarify, :math:`F_1 = f_1` is the first component of :math:`F_m`, :math:`F_2 = f_1+f_2`, etc., and :math:`F_J = 1`.  Similarly, if :math:`o_j = 1` and :math:`i < j`, then :math:`O_i = 0` and when :math:`i >= j`, :math:`O_i = 1`, and of course, :math:`O_J = 1`.  Finally, the RPS is defined to be:
 
 .. math:: \text{RPS} = \sum_{m=1}^J (F_m - O_m)^2 = \sum_{m=1}^J BS_m,
 
@@ -1018,11 +1019,23 @@ The score can be interpreted as a continuous version of the mean absolute error
 
 To calculate crps_emp_fair (bias adjusted, empirical ensemble CRPS) for each individual observation with m ensemble members:
 
-.. math:: \text{crps_emp_fair}_i = \text{crps_emp}_i - \frac{1}{2*m} *  \frac{1}{m*(m-1)} \sum_{i \ne j}|f_{i} - f_{j}|
+.. only:: latex
+
+  .. math:: \text{crps\_emp\_fair}_i = \text{crps\_emp}_i - \frac{1}{2*m} *  \frac{1}{m*(m-1)} \sum_{i \neq j}|f_{i} - f_{j}|
+
+.. only:: html
+
+  .. math:: \text{crps_emp_fair}_i = \text{crps_emp}_i - \frac{1}{2*m} *  \frac{1}{m*(m-1)} \sum_{i \neq j}|f_{i} - f_{j}|
 
 The overall CRPS_EMP_FAIR is calculated as the average of the individual measures. In equation form:
 
-.. math:: \text{CRPS_EMP_FAIR} = \text{average(crps_emp_fair) } = \frac{1}{N} \sum_{i=1}^N \text{crps_emp_fair}_i
+.. only:: latex
+
+  .. math:: \text{CRPS\_EMP\_FAIR} = \text{average(crps\_emp\_fair) } = \frac{1}{N} \sum_{i=1}^N \text{crps\_emp\_fair}_i
+
+.. only:: html
+
+  .. math:: \text{CRPS_EMP_FAIR} = \text{average(crps_emp_fair) } = \frac{1}{N} \sum_{i=1}^N \text{crps_emp_fair}_i
 
 Ensemble Mean Absolute Difference
 ---------------------------------
@@ -1031,11 +1044,23 @@ Called "SPREAD_MD" in ECNT output :numref:`table_ES_header_info_es_out_ECNT`
 
 The ensemble mean absolute difference is an alternative measure of ensemble spread. It is computed for each individual observation (denoted by a lowercase spread_md) with m ensemble members:
 
-.. math:: \text{spread_md}_i = \frac{1}{m*(m-1)} \sum_{i \ne j}|f_{i} - f_{j}|
+.. only:: latex
+
+  .. math:: \text{spread\_md}_i = \frac{1}{m*(m-1)} \sum_{i \neq j}|f_{i} - f_{j}|
+
+.. only:: html
+
+  .. math:: \text{spread_md}_i = \frac{1}{m*(m-1)} \sum_{i \neq j}|f_{i} - f_{j}|
 
 The overall SPREAD_MD is calculated as the average of the individual measures. In equation form:
 
-.. math:: \text{SPREAD_MD} = \text{average(spread_md) } = \frac{1}{N} \sum_{i=1}^N \text{spread_md}_i
+.. only:: latex
+
+  .. math:: \text{SPREAD\_MD} = \text{average(spread\_md) } = \frac{1}{N} \sum_{i=1}^N \text{spread\_md}_i
+
+.. only:: html
+
+  .. math:: \text{SPREAD_MD} = \text{average(spread_md) } = \frac{1}{N} \sum_{i=1}^N \text{spread_md}_i
 
 A perfect forecast would have ensemble mean absolute difference = 0.
 
@@ -1057,7 +1082,13 @@ Called "BIAS_RATIO" in ECNT output :numref:`table_ES_header_info_es_out_ECNT`
 
 The bias ratio (BIAS_RATIO) is computed when verifying an ensemble against gridded analyses or point observations. It is defined as the mean error (ME) of ensemble member values greater than or equal to the observation value to which they are matched divided by the absolute value of the mean error (ME) of ensemble member values less than the observation values.
 
-.. math:: \text{BIAS_RATIO} = \frac{ \text{ME}_{f >= o} }{ |\text{ME}_{f < o}| }
+.. only:: latex
+
+  .. math:: \text{BIAS\_RATIO} = \frac{ \text{ME}_{f >= o} }{ |\text{ME}_{f < o}| }
+
+.. only:: html
+
+  .. math:: \text{BIAS_RATIO} = \frac{ \text{ME}_{f >= o} }{ |\text{ME}_{f < o}| }
 
 A perfect forecast has ME = 0. Since BIAS_RATIO is computed as the high bias (ME_GE_OBS) divide by the absolute value of the low bias (ME_LT_OBS), a perfect forecast has BIAS_RATIO = 0/0, which is undefined. In practice, the high and low bias values are unlikely to be 0.
 
@@ -1070,7 +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.
 

docs/Users_Guide/release-notes.rst

@@ -9,8 +9,16 @@ When applicable, release notes are followed by the GitHub issue number which des
 enhancement, or new feature (`MET GitHub issues <https://github.com/dtcenter/MET/issues>`_).
 Important issues are listed **in bold** for emphasis.
 
-MET Version 11.0.1-rc1 release notes (20230221)
------------------------------------------------
+MET Version 11.0.2 release notes (20230330)
+-------------------------------------------
+
+* Bugfixes:
+
+   * Bugfix: Fix support for the YYYYMMDD format in NetCDF level timestrings (`#2482 <https://github.com/dtcenter/MET/issues/2482>`_).
+   * Bugfix: AERONET the lat/lon is not changed with different station ID (`#2493 <https://github.com/dtcenter/MET/issues/2493>`_).
+
+MET Version 11.0.1 release notes (20230228)
+-------------------------------------------
 
 * Bugfixes:
 
@@ -27,6 +35,7 @@ MET Version 11.0.1-rc1 release notes (20230221)
    * Bugfix: Fix ASCII2NC to handle missing NDBC buoy location information (`#2426 <https://github.com/dtcenter/MET/issues/2426>`_).
    * Bugfix: Fix the MET vx_pointdata_python library to handle MET_PYTHON_EXE for python embedding of point observations (`#2428 <https://github.com/dtcenter/MET/issues/2428>`_).
    * Bugfix: Refine the regrid dictionary's data conversion and censoring operations and fix climo time matching logic for a single monthly climo file (`#2437 <https://github.com/dtcenter/MET/issues/2437>`_).
+   * Bugfix: Fix the creation of the MET User's Guide PDF (`#2449 <https://github.com/dtcenter/MET/issues/2449>`_).  
    * Bugfix: Fix inconsistent ASCII2NC AIRNOW location lookup logic (`#2452 <https://github.com/dtcenter/MET/issues/2452>`_).  
 
 MET Version 11.0.0 release notes (20221209)

docs/conf.py

@@ -20,11 +20,11 @@
 project = 'MET'
 author = 'UCAR/NCAR, NOAA, CSU/CIRA, and CU/CIRES'
 author_list = 'Opatz, J., T. Jensen, J. Prestopnik, H. Soh, L. Goodrich, B. Brown, R. Bullock, J. Halley Gotway, K. Newman'
-version = '11.0.1-rc1'
+version = '11.0.2'
 verinfo = version
 release = f'{version}'
 release_year = '2023'
-release_date = f'{release_year}-02-21'
+release_date = f'{release_year}-03-30'
 copyright = f'{release_year}, {author}'
 
 # -- General configuration ---------------------------------------------------