Commit f96c45ff by brunner

### no bias correction - whole Hx gets updated and bias removes to many data

parent 8ffdbf8c
 ... @@ -23,6 +23,7 @@ File created on 28 Jul 2010. ... @@ -23,6 +23,7 @@ File created on 28 Jul 2010. """ """ import logging import logging import sys import numpy as np import numpy as np import numpy.linalg as la import numpy.linalg as la import da.tools.io4 as io import da.tools.io4 as io ... @@ -313,10 +314,13 @@ class Optimizer(object): ... @@ -313,10 +314,13 @@ class Optimizer(object): def serial_minimum_least_squares(self): def serial_minimum_least_squares(self): """ Make minimum least squares solution by looping over obs""" """ Make minimum least squares solution by looping over obs""" # Corrected for bias over all stations # DON'T # Corrected for bias over all stations bias = np.mean(self.obs) - np.mean(self.Hx) # obs_clean = self.obs[self.obs>0] for n in range(self.nobs): # Hx_clean = self.Hx[self.obs>0] # bias = np.mean(obs_clean) - np.mean(Hx_clean) # logging.debug('Corrected for bias %f' % (bias)) for n in range(self.nobs): # Screen for flagged observations (for instance site not found, or no sample written from model) # Screen for flagged observations (for instance site not found, or no sample written from model) if self.flags[n] != 0: if self.flags[n] != 0: ... @@ -325,14 +329,17 @@ class Optimizer(object): ... @@ -325,14 +329,17 @@ class Optimizer(object): # Screen for outliers greather than 3x model-data mismatch, only apply if obs may be rejected # Screen for outliers greather than 3x model-data mismatch, only apply if obs may be rejected # Calculate residual for rejecting the observations (corrected for bias) - res_rej # Calculate residual for rejecting the observations (corrected for bias) - res_rej res_rej = self.obs[n] - self.Hx[n] - bias # res_rej = self.obs[n] - self.Hx[n] - bias res = self.obs[n] - self.Hx[n] res = self.obs[n] - self.Hx[n] if self.may_reject[n]: if self.may_reject[n]: threshold = self.rejection_threshold * np.sqrt(self.R[n]) threshold = self.rejection_threshold * np.sqrt(self.R[n]) #if np.abs(res) > threshold + abs(bias): if np.abs(res) > threshold: if np.abs(res_rej) > 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 + abs(bias))) # 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 self.flags[n] = 2 continue continue ... ...
 ... @@ -128,28 +128,28 @@ class ObservationOperator(object): ... @@ -128,28 +128,28 @@ class ObservationOperator(object): logging.info('Starting COSMO') 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') # 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!') logging.info('COSMO done!') # Here the extraction of COSMO output starts # Here the extraction of COSMO output starts dicts = self.read_csv(dacycle) 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/') logging.info('Starting parallel extraction \m/') args = [ # args = [ (dacycle, dacycle['time.sample.start']+timedelta(hours = 24*n), dicts, rlat, rlon, path_in) # (dacycle, dacycle['time.sample.start']+timedelta(hours = 24*n), dicts, rlat, rlon, path_in) for n in range(self.days) # for n in range(self.days) ] # ] with Pool(self.days) as pool: # with Pool(self.days) as pool: pool.starmap(self.get_cosmo_data, args) # pool.starmap(self.get_cosmo_data, args) logging.info('Finished parallel extraction \m/') logging.info('Finished parallel extraction \m/') self.cat_cosmo_data(advance, dacycle) # self.cat_cosmo_data(advance, dacycle) for i in range(0,self.forecast_nmembers): for i in range(self.forecast_nmembers): idx = str(i+1).zfill(3) idx = str(i+1).zfill(3) cosmo_file = os.path.join(self.dacycle['dir.ct_save'], 'Hx_'+idx+'_%s.nc' % dacycle['time.sample.stamp']) cosmo_file = os.path.join(self.dacycle['dir.ct_save'], 'Hx_'+idx+'_%s.nc' % dacycle['time.sample.stamp']) ifile = Dataset(cosmo_file, mode='r') ifile = Dataset(cosmo_file, mode='r') ... @@ -157,6 +157,9 @@ class ObservationOperator(object): ... @@ -157,6 +157,9 @@ class ObservationOperator(object): ifile.close() ifile.close() for j,data in enumerate(zip(ids,obs,mdm)): for j,data in enumerate(zip(ids,obs,mdm)): print('j', j) print('data', data) print('model data', model_data[0,j]) f.variables['obs_num'][j] = data[0] f.variables['obs_num'][j] = data[0] f.variables['flask'][j,:] = model_data[:,j] f.variables['flask'][j,:] = model_data[:,j] f.close() f.close() ... ...
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