Implemented using DEM slope and aspect.

climax/main/downscale_infer.py

@@ -53,7 +53,7 @@ if __name__ == '__main__':
     LogConfig.init_log('Initializing ERA5 predictors.')
     Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
                        plevels=ERA5_PLEVELS)
-    Era5_ds = Era5.merge()
+    Era5_ds = Era5.merge(chunks=-1)
 
     # whether to use digital elevation model
     if DEM:
@@ -63,9 +63,10 @@ if __name__ == '__main__':
         # read elevation and compute slope and aspect
         dem = ERA5Dataset.dem_features(dem, {'time': Era5_ds.time})
 
-        # check wether to use slope and aspect
-        if not DEM_FEATURES:
-            dem = dem.drop_vars(['slope', 'aspect'])
+        # read elevation and compute slope and aspect
+        dem = ERA5Dataset.dem_features(
+            dem, {'y': Era5_ds.y, 'x': Era5_ds.x},
+            add_coord={'time': Era5_ds.time})
 
         # add dem to set of predictor variables
         Era5_ds = xr.merge([Era5_ds, dem])

climax/main/downscale_train.py

@@ -61,7 +61,7 @@ if __name__ == '__main__':
         LogConfig.init_log('Initializing ERA5 predictors.')
         Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
                            plevels=ERA5_PLEVELS)
-        Era5_ds = Era5.merge()
+        Era5_ds = Era5.merge(chunks=-1)
 
         # initialize OBS predictand dataset
         LogConfig.init_log('Initializing observations for predictand: {}'
@@ -86,14 +86,15 @@ if __name__ == '__main__':
             dem = search_files(DEM_PATH, '^eu_dem_v11_stt.nc$').pop()
 
             # read elevation and compute slope and aspect
-            dem = ERA5Dataset.dem_features(dem, {'time': Era5_ds.time})
+            dem = ERA5Dataset.dem_features(
+                dem, {'y': Era5_ds.y, 'x': Era5_ds.x},
+                add_coord={'time': Era5_ds.time})
 
             # check wether to use slope and aspect
             if not DEM_FEATURES:
                 dem = dem.drop_vars(['slope', 'aspect'])
 
             # add dem to set of predictor variables
-            LOGGER.info('HI')
             Era5_ds = xr.merge([Era5_ds, dem])
 
         # initialize training data