updated make pages automation

README.rst

@@ -2,28 +2,30 @@
 Welcome to the NREL Global Climate Model Evaluation Repository
 ##############################################################
 
-The interplay between energy, climate, and weather is becoming more complex as
-our energy system becomes more sensitive to the impacts of weather events and
-climate change due to increasing contributions of renewable energy generation
-and electrified end uses. Energy system analyses commonly rely on
-meteorological inputs to estimate renewable energy generation and energy
-demand; however, these inputs rarely represent the estimated impacts of future
-climate change. Climate models and publicly available climate change datasets
-can be used for this purpose, but the selection of inputs from the myriad of
-available models and datasets is a nuanced and subjective process. In this
-work, we assess datasets from various global climate models (GCMs) from the
-Coupled Model Intercomparison Project (CMIP6), evaluating their skills with
-respect to the historical climate and comparing their future projections of
-climate change. Previous work has presented similar GCM evaluations, but none
-have presented variables and metrics specifically intended for comprehensive
-energy systems analysis including impacts on energy demand, thermal cooling,
-hydropower, water availability, solar energy generation, and wind energy
-generation. We focus on GCM output meteorological variables that directly
-affect these energy system components including the representation of extreme
-values that can drive grid resilience events. The objective of this work is
-not to recommend the best climate model and dataset for a given analysis, but
-instead to provide a reference to facilitate the selection of climate models
-and datasets in subsequent work.
+The interplay between energy, climate, and weather is becoming more complex due
+to increasing contributions of renewable energy generation, energy storage,
+electrified end uses, and the increasing frequency of extreme weather events.
+Energy system analyses commonly rely on meteorological inputs to estimate
+renewable energy generation and energy demand; however, these inputs rarely
+represent the estimated impacts of future climate change. Climate models and
+publicly available climate change datasets can be used for this purpose, but
+the selection of inputs from the myriad of available models and datasets is a
+nuanced and subjective process. In this work, we assess datasets from various
+global climate models (GCMs) from the Coupled Model Intercomparison Project
+(CMIP6). We present evaluations of their skills with respect to the historical
+climate and comparisons of their future projections of climate change. We
+present the results for different climatic and energy system regions and
+include interactive figures in the accompanying software repository. Previous
+work has presented similar GCM evaluations, but none have presented variables
+and metrics specifically intended for comprehensive energy systems analysis
+including impacts on energy demand, thermal cooling, hydropower, water
+availability, solar energy generation, and wind energy generation. We focus on
+GCM output meteorological variables that directly affect these energy system
+components including the representation of extreme values that can drive grid
+resilience events. The objective of this work is not to recommend the best
+climate model and dataset for a given analysis, but instead to provide a
+reference to facilitate the selection of climate models and datasets in
+subsequent work.
 
 The NREL software record for this repository is SWR-24-37
 

docs/make_interactive_pages.py

@@ -4,7 +4,19 @@
 """
 import glob
 
-REGIONS = ['CONUS']
+REGIONS = ['CONUS',
+           'Northeast', 'Northern Rockies and Plains',
+           'Northwest', 'Ohio Valley', 'South', 'Southeast',
+           'Southwest', 'Upper Midwest', 'West',
+           'MRO', 'NPCC', 'RF', 'SERC', 'Texas RE', 'WECC',
+           'Atlantic', 'Pacific', 'Gulf']
+
+NOAA = ['Northeast', 'Northern Rockies and Plains',
+        'Northwest', 'Ohio Valley', 'South', 'Southeast',
+        'Southwest', 'Upper Midwest', 'West']
+NERC = ['MRO', 'NPCC', 'RF', 'SERC', 'Texas RE', 'WECC']
+OFFSHORE = ['Atlantic', 'Pacific', 'Gulf']
+UTILITIES = ['TVA', 'Southern Company']
 
 VARS = {'t2m': 'Change in Temperature (Celsius)',
         't2m_degf': 'Change in Temperature (Fahrenheit)',
@@ -22,21 +34,41 @@
 if __name__ == '__main__':
     index = ['.. toctree::\n',
              '   :hidden:\n\n',
-             '   Home page <self>\n',
-             '\n.. include:: ../../README.rst\n']
+             '   Home page <self>\n']
 
     for region in REGIONS:
         tag = region.lower().replace(' ', '_')
         fps = sorted(glob.glob(f'./source/_static/trend_plots/{tag}*.html'))
         region_rst = f'./source/regions/{tag}.rst'
 
+        if region in NOAA:
+            region = f'NOAA Region {region}'
+        elif region in NERC:
+            region = f'NERC Region {region}'
+        elif region in OFFSHORE:
+            region = f'Offshore Wind Region {region}'
+        elif region in UTILITIES:
+            region = f'Utility Partner {region}'
+
         region_pointer = region_rst.replace('./source/', '')
-        index.insert(3, f'   {region} <{region_pointer}>\n')
+        index.append(f'   {region} <{region_pointer}>\n')
 
         with open(region_rst, 'w') as f:
             f.write(f'{"#"*len(region)}\n')
             f.write(f'{region}\n')
             f.write(f'{"#"*len(region)}\n\n')
+
+            title = f'Map of {region}'
+            f.write(f'\n{title}\n')
+            f.write(f'{"="*len(title)}\n\n')
+            f.write(f'.. image:: ../_static/region_maps/map_{tag}.png\n')
+
+            title = 'GCM Historical Skill Summary'
+            f.write(f'\n{title}\n')
+            f.write(f'{"="*len(title)}\n\n')
+            f.write('.. raw:: html\n')
+            f.write(f'   :file: ../_static/skill_tables/skill_{tag}.html\n')
+
             for var, title in VARS.items():
                 f.write(f'\n{title}\n')
                 f.write(f'{"="*len(title)}\n\n')
@@ -45,3 +77,4 @@
 
         with open('./source/index.rst', 'w') as f:
             f.writelines(index)
+            f.write('\n.. include:: ../../README.rst\n')