def plot_swe_bfes(runconfig_rea, runconfig_gcm, vname_model="I5", season_to_months=None,
bmp_info=None, axes_list=None):
seasonal_clim_fields_rea = analysis.get_seasonal_climatology_for_runconfig(run_config=runconfig_rea,
varname=vname_model, level=0,
season_to_months=season_to_months)
seasonal_clim_fields_gcm = analysis.get_seasonal_climatology_for_runconfig(run_config=runconfig_gcm,
varname=vname_model, level=0,
season_to_months=season_to_months)
lons = bmp_info.lons.copy()
lons[lons > 180] -= 360
assert len(seasonal_clim_fields_rea) > 0
season_to_err = OrderedDict()
for season, field in seasonal_clim_fields_rea.items():
rea = field
gcm = seasonal_clim_fields_gcm[season]
# Mask oceans and lakes
season_to_err[season] = maskoceans(lons, bmp_info.lats, gcm - rea)
assert hasattr(season_to_err[season], "mask")
plot_performance_err_with_cru.plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model, basemap_info=bmp_info,
axes_list=axes_list)
def compare_vars(vname_model="TT",
vname_obs="tmp",
r_config=None,
season_to_months=None,
obs_path=None,
nx_agg=5,
ny_agg=5,
bmp_info_agg=None,
axes_list=None):
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(
field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if obs_path is None:
cru = CRUDataManager(var_name=vname_obs)
else:
cru = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(
cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=1)
assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs_interp:
if vname_model == "PR":
# * 10, because in the UDEL dataset, precipitation is in cm
season_to_err[season] = season_to_clim_fields_model_agg[season] - \
seasonal_clim_fields_obs_interp[season] * MM_PER_CM
else:
season_to_err[season] = season_to_clim_fields_model_agg[
season] - seasonal_clim_fields_obs_interp[season]
plot_performance_err_with_cru.plot_seasonal_mean_biases(
season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
def compare_vars(vname_model, vname_to_obs, r_config, season_to_months,
bmp_info_agg, axes_list):
season_to_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
for season, field in season_to_clim_fields_model.items():
print(field.shape)
if vname_model == "PR":
field *= 1.0e3 * 24 * 3600
seasonal_clim_fields_obs = vname_to_obs[vname_model]
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs:
season_to_err[season] = season_to_clim_fields_model[
season] - seasonal_clim_fields_obs[season]
season_to_err[season] = maskoceans(lons,
bmp_info_agg.lats,
season_to_err[season],
inlands=False)
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
return cs
def compare_vars(vname_model, vname_to_obs, r_config, season_to_months, bmp_info_agg, axes_list):
season_to_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
for season, field in season_to_clim_fields_model.items():
print(field.shape)
if vname_model == "PR":
field *= 1.0e3 * 24 * 3600
seasonal_clim_fields_obs = vname_to_obs[vname_model]
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs:
season_to_err[season] = season_to_clim_fields_model[season] - seasonal_clim_fields_obs[season]
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats, season_to_err[season], inlands=False)
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
return cs
def compare_vars(vname_model, vname_to_obs, r_config, season_to_months,
bmp_info_agg, axes_list):
season_to_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
for season, field in season_to_clim_fields_model.items():
print(field.shape)
if vname_model == "PR":
field *= 1.0e3 * 24 * 3600
seasonal_clim_fields_obs = vname_to_obs[vname_model]
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err = OrderedDict()
print(
"-------------var: {} (PE with anusplin)---------------------".format(
vname_model))
for season in seasonal_clim_fields_obs:
seasonal_clim_fields_obs[season] = np.ma.masked_where(
np.isnan(seasonal_clim_fields_obs[season]),
seasonal_clim_fields_obs[season])
season_to_err[season] = season_to_clim_fields_model[
season] - seasonal_clim_fields_obs[season]
season_to_err[season] = maskoceans(lons,
bmp_info_agg.lats,
season_to_err[season],
inlands=False)
season_to_err[season] = np.ma.masked_where(
np.isnan(season_to_err[season]), season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season, good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- anuplin ---")
print("{}: {} %".format(
season,
good_vals.mean() / seasonal_clim_fields_obs[season]
[~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
return cs
def compare_vars(vname_model="TT", vname_obs="tmp", r_config=None,
season_to_months=None,
obs_path=None, nx_agg=5, ny_agg=5, bmp_info_agg=None, axes_list=None):
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if obs_path is None:
cru = CRUDataManager(var_name=vname_obs)
else:
cru = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats, nneighbours=1)
assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs_interp:
if vname_model == "PR":
# * 10, because in the UDEL dataset, precipitation is in cm
season_to_err[season] = season_to_clim_fields_model_agg[season] - \
seasonal_clim_fields_obs_interp[season] * MM_PER_CM
else:
season_to_err[season] = season_to_clim_fields_model_agg[season] - seasonal_clim_fields_obs_interp[season]
plot_performance_err_with_cru.plot_seasonal_mean_biases(season_to_error_field=season_to_err, varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
def compare_vars(vname_model, vname_to_obs, r_config, season_to_months, bmp_info_agg, axes_list):
season_to_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
for season, field in season_to_clim_fields_model.items():
print(field.shape)
if vname_model == "PR":
field *= 1.0e3 * 24 * 3600
seasonal_clim_fields_obs = vname_to_obs[vname_model]
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err = OrderedDict()
print("-------------var: {} (PE with anusplin)---------------------".format(vname_model))
for season in seasonal_clim_fields_obs:
seasonal_clim_fields_obs[season] = np.ma.masked_where(np.isnan(seasonal_clim_fields_obs[season]),
seasonal_clim_fields_obs[season])
season_to_err[season] = season_to_clim_fields_model[season] - seasonal_clim_fields_obs[season]
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats, season_to_err[season], inlands=False)
season_to_err[season] = np.ma.masked_where(np.isnan(season_to_err[season]), season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season,
good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- anuplin ---")
print("{}: {} %".format(season, good_vals.mean() / seasonal_clim_fields_obs[season][~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err, varname=vname_model, basemap_info=bmp_info_agg,
axes_list=axes_list)
return cs
