no bias correction, not needed

da/cosmo/base_optimizer.py

@@ -337,7 +337,7 @@ class Optimizer(object):
                 if np.abs(res) > threshold:
 #                if np.abs(res) > threshold + abs(bias):
 #                if np.abs(res_rej) > threshold:
-                    logging.debug('Rejecting observation (%s,%i) because residual (%f) exceeds threshold (%f)' % (self.sitecode[n], self.obs_ids[n], res, threshold + abs(bias)))
+                    logging.debug('Rejecting observation (%s,%i) because residual (%f) exceeds threshold (%f)' % (self.sitecode[n], self.obs_ids[n], res, threshold))
                 #    logging.debug('Rejecting observation (%s,%i) because residual (%f) exceeds threshold (%f)' % (self.sitecode[n], self.obs_ids[n], res_rej, threshold))
 
                     self.flags[n] = 2

da/cosmo/observationoperator_cosmo.py

@@ -128,26 +128,26 @@ class ObservationOperator(object):
 
         logging.info('Starting COSMO')
 
-#        os.system('python run_chain.py '+self.dacycle['run.name']+' '+abs_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc int2lm post_int2lm oae octe online_vprm cosmo -f')
+        os.system('python run_chain.py '+self.dacycle['run.name']+' '+abs_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc int2lm post_int2lm oae octe online_vprm cosmo -f')
 
         logging.info('COSMO done!')
 
         # Here the extraction of COSMO output starts
         dicts = self.read_csv(dacycle)
-  #      rlat, rlon, dicts, path_in = self.get_hhl_data(dacycle, lag, 'lffd'+abs_start_time+'c.nc', dicts, starth, endh)
+        rlat, rlon, dicts, path_in = self.get_hhl_data(dacycle, lag, 'lffd'+abs_start_time+'c.nc', dicts, starth, endh)
         logging.info('Starting parallel extraction \m/')
 
-#        args = [
- #           (dacycle, dacycle['time.sample.start']+timedelta(hours = 24*n), dicts, rlat, rlon, path_in)
-  #          for n in range(self.days)
-   #     ]
+        args = [
+            (dacycle, dacycle['time.sample.start']+timedelta(hours = 24*n), dicts, rlat, rlon, path_in)
+            for n in range(self.days)
+        ]
 
-#        with Pool(self.days) as pool:
- #           pool.starmap(self.get_cosmo_data, args)
+        with Pool(self.days) as pool:
+            pool.starmap(self.get_cosmo_data, args)
 
         logging.info('Finished parallel extraction \m/')
 
-#        self.cat_cosmo_data(advance, dacycle)
+        self.cat_cosmo_data(advance, dacycle)
 
         for i in range(self.forecast_nmembers):
             idx = str(i+1).zfill(3)