lat = tmp[:, :, 1]
lon = tmp[:, :, 0]
#Exclude areas outside the radar domain.
radar_mask = cmf.distance_range_mask(lon_radar, lat_radar, radar_range,
lon, lat)
#=========================================================
# READ THE DATA
#=========================================================
my_file = basedir + '/' + my_exp + '/time_mean/' + filetype + '/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment) + '_rmse' + '.grd'
rmse[my_exp] = np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=True), 4, 2), 2)
rmse[my_exp][np.isnan(rmse[my_exp])] = undef
rmse[my_exp] = cf.gaussian_filter(field0=rmse[my_exp],
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
rmse[my_exp][rmse[my_exp] == undef] = np.nan
print('RMSE values')
print(my_exp)
print(np.min(rmse[my_exp]), np.max(rmse[my_exp]))
my_file = basedir + '/' + my_exp + '/time_mean/' + filetype + '/' + '/moment0001_mean.grd'
while ( ctime <= etime ) :
print( ctime )
print ( 'Reading data')
#=========================================================
# READ THE DATA
#=========================================================
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/' + filetype + '/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
print('Reading update comparisson for ',var_obs,' and ',var_upd)
#Read all the variables and levels at once
tmp_parameter[:,:,:,it] = np.squeeze( ctlr.read_data( my_file , ctl_dict , undef2nan = True ) )
#print( np.nanmin(tmp_parameter[:,:,:,it]) , np.nanmax( tmp_parameter[:,:,:,it] ) )
ctime = ctime + delta
it = it + 1
print ( "Finish time loop" )
rmse = np.sqrt( np.squeeze( np.nanmean( np.power( tmp_parameter , 2) , 3 ) ) )
bias = np.squeeze( np.nanmean( tmp_parameter , 3 ) )
print('RMSE ', np.nanmin(rmse) , np.nanmax(rmse) )
print('BIAS ', np.nanmin(bias) , np.nanmax(bias) )
# READ THE DATA
#=========================================================
parameter_5MIN = []
parameter_30SEC = []
for iv, my_var in enumerate(obs_variables):
var_obs = obs_variables[iv]
var_upd = upd_variables[iv]
my_file = basedir + '/LE_D1_1km_5min/time_mean/guesgp/update_comp_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment[iv]) + '_kld' + '.grd'
parameter_5MIN.append(
np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=False), 4,
2), 2))
my_file = basedir + '/LE_D1_1km_30sec/time_mean/guesgp/update_comp_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment[iv]) + '_kld' + '.grd'
parameter_30SEC.append(
np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=False), 4,
2), 2))
parameter_5MIN[iv] = cf.gaussian_filter(field0=parameter_5MIN[iv],
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
parameter_30SEC[iv] = cf.gaussian_filter(field0=parameter_30SEC[iv],
print(ctime)
print('Reading data')
#=========================================================
# READ THE DATA
#=========================================================
my_file = basedir + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S"
) + '/' + my_file_type + '/' + '/' + file_name + '.grd'
#Read all the variables and levels at once
tmp_parameter[:, :, :, it] = np.squeeze(
ctlr.read_data(my_file, ctl_dict, undef2nan=True))
print(np.nanmin(tmp_parameter[:, :, :, it]),
np.nanmax(tmp_parameter[:, :, :, it]))
ctime = ctime + delta
it = it + 1
print("Finish time loop")
parameter = np.squeeze(np.nanmean(tmp_parameter, 3))
print(np.nanmin(parameter), np.nanmax(parameter))
my_file = outputdir + '/' + file_name + '_mean' + '.grd'
print(ctime)
print('Reading data')
#=========================================================
# READ THE DATA
#=========================================================
my_file = basedir + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S"
) + '/' + my_file_type + '/' + '/' + file_name + '.grd'
#Read all the variables and levels at once
tmp_parameter[:, :, :,
it] = np.squeeze(ctlr.read_data(my_file, ctl_dict))
print(np.min(tmp_parameter[:, :, :, it]),
np.max(tmp_parameter[:, :, :, it]))
ctime = ctime + delta
it = it + 1
print("Finish time loop")
tmp_parameter[tmp_parameter == undef] = np.nan
print(np.nanmin(tmp_parameter), np.nanmax(tmp_parameter))
parameter = np.squeeze(np.nanmean(tmp_parameter, 3))
kld_norain = np.zeros((nz,ntimes))
while ( ctime <= etime ) :
print( ctime )
print ('Reading data')
#=========================================================
# READ THE DATA
#=========================================================
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/' + filetype + '/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
#Read all the variables and levels at once
parameter = ctlr.read_data( my_file , ctl_dict , undef2nan = True )
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/' + filetype + '/update_comp_kld_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
#Read all the variables and levels at once
kld = ctlr.read_data( my_file , ctl_dict , undef2nan = True )
my_file=basedir + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/moment0001.grd'
max_dbz = np.squeeze( np.nanmax( ctlr.read_data_grads( my_file , ctl_dict_2 , undef2nan = True )['dbz'] , 2 ) )
#Several mask can be defined here.
rain_mask = np.logical_and( max_dbz > rain_threshold , radar_mask )
norain_mask = np.logical_and( max_dbz < norain_threshold , radar_mask )
print(ctime)
print('Reading data')
#=========================================================
# READ THE DATA
#=========================================================
my_file = basedir + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S"
) + '/' + my_file_type + '/' + '/' + file_name + '.grd'
#Read all the variables and levels at once
tmp_parameter[:, :, :, it] = np.squeeze(
ctlr.read_data(my_file, ctl_dict, undef2nan=True))
my_file = basedir + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S") + '/guesgp/moment0001.grd'
gues = np.squeeze(ctlr.read_data(my_file, ctl_dict,
undef2nan=True))
my_file = basedir + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S") + '/analgp/moment0001.grd'
anal = np.squeeze(ctlr.read_data(my_file, ctl_dict,
undef2nan=True))
#Remove the grid points in which the prior has not been updated by the observations.
tmp_parameter[:, :, :, it][np.abs(gues - anal) < 1.0e-7] = np.nan
#Read all the variables and levels at once
ensemble_mean = ctlr.read_data_grads( my_file , ctl_dict_2 , undef2nan = True )
max_dbz = np.squeeze( np.nanmax( np.delete( ensemble_mean['dbz'] , 4 , 2) , 2 ) )
rain_mask = np.logical_and( max_dbz > rain_threshold , radar_mask )
norain_mask = np.logical_and( max_dbz < norain_threshold , radar_mask )
#=========================================================
# READ THE DATA
#=========================================================
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
update_mean_diff = np.squeeze( ctlr.read_data( my_file , ctl_dict , undef2nan = True ) )
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/update_comp_updated_mean_kf_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
update_mean_kf = np.squeeze( ctlr.read_data( my_file , ctl_dict , undef2nan = True ) )
my_file=basedir + '/' + my_exp + '/' + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/update_comp_kld_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
update_kld = np.squeeze( ctlr.read_data( my_file , ctl_dict , undef2nan = True ) )
update_mean_bias = np.copy(update_mean_diff)
update_mean_diff = np.power( np.delete( update_mean_diff , 4 ,2 ) , 2 )
update_mean_kf = np.power( np.delete( update_mean_kf - np.squeeze(ensemble_mean[ var_upd ]) , 4 ,2 ) , 2 )
update_kld = np.delete( update_kld , 4 , 2 )
update_mean_diff[ update_mean_diff == 0.0 ] = np.nan
update_mean_kf[ update_mean_kf == 0.0 ] = np.nan
mean_diff = []
kld = []
my_file=basedir + expnames[icase] + ctimes[icase].strftime("%Y%m%d%H%M%S") + '/guesgp/moment0001.grd'
m01=ctlr.read_data_grads(my_file,ctl_dict_2,masked=False,undef2nan=True)
for iv,my_var in enumerate( obs_variables ) :
var_obs = obs_variables[iv]
var_upd = upd_variables[iv]
my_file=basedir + '/' + expnames[icase] + '/' + ctimes[icase].strftime("%Y%m%d%H%M%S") +'/guesgp/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
mean_diff.append( np.delete( ctlr.read_data(my_file,ctl_dict,undef2nan=False) , 4 , 2 ) )
my_file=basedir + '/' + expnames[icase] + '/' + ctimes[icase].strftime("%Y%m%d%H%M%S") +'/guesgp/update_comp_kld_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(nbv) + '_obs_inc_' + str(obs_increment[iv]) + '.grd'
kld.append( np.delete( ctlr.read_data(my_file,ctl_dict,undef2nan=False) , 4 , 2 ) )
print(var_obs,var_upd,np.min(mean_diff[iv]),np.max(mean_diff[iv]))
print(var_obs,var_upd,np.min(kld[iv]),np.max(kld[iv]))
for ii in range( mean_diff[iv].shape[2] ) :
mean_diff[iv][:,:,ii][np.logical_not( mask )]=np.nan
kld[iv][:,:,ii][np.logical_not( mask )]=np.nan
mean_diff[iv] = np.squeeze( np.nanmean( mean_diff[iv] , 1 ) )
kld[iv] = np.squeeze( np.nanmean( kld[iv] , 1 ) )
dbz=np.squeeze(np.delete(m01['dbz'],4,2))