def Taylor_diagram_spatial_pattern_of_multiyear_climatology(
obs_dataset, obs_name, model_datasets, model_names, file_name):
# calculate climatological mean fields
obs_clim_dataset = ds.Dataset(obs_dataset.lats, obs_dataset.lons,
obs_dataset.times,
utils.calc_temporal_mean(obs_dataset))
model_clim_datasets = []
for dataset in model_datasets:
model_clim_datasets.append(
ds.Dataset(dataset.lats, dataset.lons, dataset.times,
utils.calc_temporal_mean(dataset)))
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(
obs_clim_dataset, # Climatological mean of reference dataset for the evaluation
model_clim_datasets, # list of climatological means from model datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data,
model_names,
obs_name,
file_name,
pos='upper right',
frameon=False)
def Taylor_diagram_spatial_pattern_of_multiyear_climatology(
obs_dataset, obs_name, model_datasets, model_names, file_name):
# calculate climatological mean fields
obs_clim_dataset = ds.Dataset(obs_dataset.lats, obs_dataset.lons,
obs_dataset.times,
utils.calc_temporal_mean(obs_dataset))
model_clim_datasets = []
for dataset in model_datasets:
model_clim_datasets.append(
ds.Dataset(dataset.lats, dataset.lons, dataset.times,
utils.calc_temporal_mean(dataset)))
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(
obs_clim_dataset, # Climatological mean of reference dataset for the evaluation
model_clim_datasets, # list of climatological means from model datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(
taylor_data,
model_names,
obs_name,
file_name,
pos='upper right',
frameon=False)
def Taylor_diagram_spatial_pattern_of_multiyear_climatology(
obs_dataset, obs_name, model_datasets, model_names, file_name):
# calculate climatological mean fields
obs_dataset.values = utils.calc_temporal_mean(obs_dataset)
for dataset in model_datasets:
dataset.values = utils.calc_temporal_mean(dataset)
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(
obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data,
model_names,
obs_name,
file_name,
pos='upper right',
frameon=False)
def test_returned_mean(self):
mean_values = np.array([[22.5, 23.5, 24.5],
[25.5, 26.5, 27.5],
[28.5, 29.5, 30.5]])
result = utils.calc_temporal_mean(self.test_dataset)
np.testing.assert_array_equal(result, mean_values)
def Map_plot_bias_of_multiyear_climatology(obs_dataset, obs_name, model_datasets, model_names,
file_name, row, column):
'''Draw maps of observed multi-year climatology and biases of models"'''
# calculate climatology of observation data
obs_clim = utils.calc_temporal_mean(obs_dataset)
# determine the metrics
map_of_bias = metrics.TemporalMeanBias()
# create the Evaluation object
bias_evaluation = Evaluation(obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[map_of_bias, map_of_bias])
# run the evaluation (bias calculation)
bias_evaluation.run()
rcm_bias = bias_evaluation.results[0]
fig = plt.figure()
lat_min = obs_dataset.lats.min()
lat_max = obs_dataset.lats.max()
lon_min = obs_dataset.lons.min()
lon_max = obs_dataset.lons.max()
string_list = list(string.ascii_lowercase)
ax = fig.add_subplot(row,column,1)
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=False)
lons, lats = np.meshgrid(obs_dataset.lons, obs_dataset.lats)
x,y = m(lons, lats)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,y,obs_clim,levels = plotter._nice_intervals(obs_dataset.values, 10), extend='both',cmap='PuOr')
ax.annotate('(a) \n' + obs_name,xy=(lon_min, lat_min))
cax = fig.add_axes([0.02, 1.-float(1./row), 0.01, 1./row*0.6])
plt.colorbar(max, cax = cax)
clevs = plotter._nice_intervals(rcm_bias, 11)
for imodel in np.arange(len(model_datasets)):
ax = fig.add_subplot(row, column,2+imodel)
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=False)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,y,rcm_bias[imodel,:],levels = clevs, extend='both', cmap='RdBu_r')
ax.annotate('('+string_list[imodel+1]+') \n '+model_names[imodel],xy=(lon_min, lat_min))
cax = fig.add_axes([0.91, 0.1, 0.015, 0.8])
plt.colorbar(max, cax = cax)
plt.subplots_adjust(hspace=0.01,wspace=0.05)
plt.show()
fig.savefig(file_name,dpi=600,bbox_inches='tight')
def Taylor_diagram_spatial_pattern_of_multiyear_climatology(obs_dataset, obs_name, model_datasets, model_names,
file_name):
# calculate climatological mean fields
obs_dataset.values = utils.calc_temporal_mean(obs_dataset)
for dataset in model_datasets:
dataset.values = utils.calc_temporal_mean(dataset)
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data, model_names, obs_name, file_name, pos='upper right',frameon=False)
temporal_resolution='monthly')
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
#Regrid
print("... regrid")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member],
new_lats, new_lons)
#find the mean values
#way to get the mean. Note the function exists in util.py as def calc_climatology_year(dataset):
CRU31.values = utils.calc_temporal_mean(CRU31)
#make the model ensemble
target_datasets_ensemble = dsp.ensemble(target_datasets)
target_datasets_ensemble.name = "ENS"
#append to the target_datasets for final analysis
target_datasets.append(target_datasets_ensemble)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member].values = utils.calc_temporal_mean(
target_datasets[member])
allNames = []
for target in target_datasets:
def Map_plot_bias_of_multiyear_climatology(obs_dataset,
obs_name,
model_datasets,
model_names,
file_name,
row,
column,
map_projection=None):
'''Draw maps of observed multi-year climatology and biases of models"'''
# calculate climatology of observation data
obs_clim = utils.calc_temporal_mean(obs_dataset)
# determine the metrics
map_of_bias = metrics.TemporalMeanBias()
# create the Evaluation object
bias_evaluation = Evaluation(
obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[map_of_bias, map_of_bias])
# run the evaluation (bias calculation)
bias_evaluation.run()
rcm_bias = bias_evaluation.results[0]
fig = plt.figure()
lat_min = obs_dataset.lats.min()
lat_max = obs_dataset.lats.max()
lon_min = obs_dataset.lons.min()
lon_max = obs_dataset.lons.max()
string_list = list(string.ascii_lowercase)
ax = fig.add_subplot(row, column, 1)
if map_projection == 'npstere':
m = Basemap(ax=ax,
projection='npstere',
boundinglat=lat_min,
lon_0=0,
resolution='l',
fix_aspect=False)
else:
m = Basemap(ax=ax,
projection='cyl',
llcrnrlat=lat_min,
urcrnrlat=lat_max,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
resolution='l',
fix_aspect=False)
lons, lats = np.meshgrid(obs_dataset.lons, obs_dataset.lats)
x, y = m(lons, lats)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,
y,
obs_clim,
levels=plotter._nice_intervals(obs_dataset.values, 10),
extend='both',
cmap='rainbow')
ax.annotate('(a) \n' + obs_name, xy=(lon_min, lat_min))
cax = fig.add_axes([0.02, 1. - float(1. / row), 0.01, 1. / row * 0.6])
plt.colorbar(max, cax=cax)
clevs = plotter._nice_intervals(rcm_bias, 11)
for imodel in np.arange(len(model_datasets)):
ax = fig.add_subplot(row, column, 2 + imodel)
if map_projection == 'npstere':
m = Basemap(ax=ax,
projection='npstere',
boundinglat=lat_min,
lon_0=0,
resolution='l',
fix_aspect=False)
else:
m = Basemap(ax=ax,
projection='cyl',
llcrnrlat=lat_min,
urcrnrlat=lat_max,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
resolution='l',
fix_aspect=False)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,
y,
rcm_bias[imodel, :],
levels=clevs,
extend='both',
cmap='RdBu_r')
ax.annotate('(' + string_list[imodel + 1] + ') \n ' +
model_names[imodel],
xy=(lon_min, lat_min))
cax = fig.add_axes([0.91, 0.1, 0.015, 0.8])
plt.colorbar(max, cax=cax)
plt.subplots_adjust(hspace=0.01, wspace=0.05)
fig.savefig(file_name, dpi=600, bbox_inches='tight')
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
target_datasets[member] = dsp.temporal_rebin(target_datasets[member], datetime.timedelta(days=30))
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
#Regrid
print("... regrid")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
#find the mean values
#way to get the mean. Note the function exists in util.py as def calc_climatology_year(dataset):
CRU31.values = utils.calc_temporal_mean(CRU31)
#make the model ensemble
target_datasets_ensemble = dsp.ensemble(target_datasets)
target_datasets_ensemble.name="ENS"
#append to the target_datasets for final analysis
target_datasets.append(target_datasets_ensemble)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member].values = utils.calc_temporal_mean(target_datasets[member])
allNames =[]
for target in target_datasets:
allNames.append(target.name)
FILE_2 = "AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc"
# Filename for the output image/plot (without file extension)
OUTPUT_PLOT = "wrf_bias_compared_to_knmi"
FILE_1_PATH = path.join('/tmp', FILE_1)
FILE_2_PATH = path.join('/tmp', FILE_2)
if not path.exists(FILE_1_PATH):
urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1_PATH)
if not path.exists(FILE_2_PATH):
urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2_PATH)
""" Step 1: Load Local NetCDF Files into OCW Dataset Objects """
print("Loading %s into an OCW Dataset Object" % (FILE_1_PATH,))
knmi_dataset = local.load_file(FILE_1_PATH, "tasmax")
print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))
print("Loading %s into an OCW Dataset Object" % (FILE_2_PATH,))
wrf_dataset = local.load_file(FILE_2_PATH, "tasmax")
print("WRF_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_dataset.values.shape,))
""" Step 2: Calculate seasonal average """
print("Calculate seasonal average")
knmi_DJF_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=12, month_end=2, target_dataset=knmi_dataset))
wrf_DJF_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=12, month_end=2, target_dataset=wrf_dataset))
print("Seasonally averaged KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_DJF_mean.shape,))
print("Seasonally averaged wrf_Dataset.values shape: (times, lats, lons) - %s \n" % (wrf_DJF_mean.shape,))
knmi_JJA_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=6, month_end=8, target_dataset=knmi_dataset))
wrf_JJA_mean = utils.calc_temporal_mean(dsp.temporal_subset(month_start=6, month_end=8, target_dataset=wrf_dataset))
