def get_seasonal_clim_obs_data(rconfig=None,
vname="TT",
bmp_info=None,
season_to_months=None,
obs_path=None,
nx_agg=None,
ny_agg=None):
# Number of points for aggregation
"""
return aggregated BasemapInfo object corresponding to the CRU resolution
:param rconfig:
:param vname:
:param bmp_info: BasemapInfo object for the model field (will be upscaled to the CRU resolution)
:param season_to_months:
"""
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(
file_path=rconfig.data_path)
if nx_agg is not None:
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
else:
bmp_info_agg = bmp_info
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
if obs_path is None:
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=rconfig.start_year,
end_year=rconfig.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")
return bmp_info_agg, seasonal_clim_fields_obs_interp
def main(dfs_var_name="t2", cru_var_name="tmp",
dfs_folder="/home/huziy/skynet3_rech1/NEMO_OFFICIAL/DFS5.2_interpolated",
cru_file = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc"):
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
#year range is inclusive [start_year, end_year]
start_year = 1981
end_year = 2009
season_name_to_months = OrderedDict([
("Winter", (1, 2, 12)),
("Spring", list(range(3, 6))),
("Summer", list(range(6, 9))),
("Fall", list(range(9, 12)))])
cru_t_manager = CRUDataManager(var_name=cru_var_name, path=cru_file)
cru_lons, cru_lats = cru_t_manager.lons2d, cru_t_manager.lats2d
#get seasonal means (CRU)
season_to_mean_cru = cru_t_manager.get_seasonal_means(season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
#get seasonal means Drakkar
dfs_manager = DFSDataManager(folder_path=dfs_folder, var_name=dfs_var_name)
season_to_mean_dfs = dfs_manager.get_seasonal_means(season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
dfs_lons, dfs_lats = dfs_manager.get_lons_and_lats_2d()
xt, yt, zt = lat_lon.lon_lat_to_cartesian(dfs_lons.flatten(), dfs_lats.flatten())
xs, ys, zs = lat_lon.lon_lat_to_cartesian(cru_lons.flatten(), cru_lats.flatten())
ktree = cKDTree(data=list(zip(xs, ys, zs)))
dists, inds = ktree.query(list(zip(xt, yt, zt)))
season_to_err = OrderedDict()
for k in season_to_mean_dfs:
interpolated_cru = season_to_mean_cru[k].flatten()[inds].reshape(dfs_lons.shape)
if dfs_var_name.lower() == "t2":
#interpolated_cru += 273.15
season_to_mean_dfs[k] -= 273.15
elif dfs_var_name.lower() == "precip": # precipitation in mm/day
season_to_mean_dfs[k] *= 24 * 60 * 60
season_to_err[k] = season_to_mean_dfs[k] #- interpolated_cru
season_indicator = "-".join(sorted(season_to_err.keys()))
fig_path = os.path.join(NEMO_IMAGES_DIR, "{3}_errors_{0}-{1}_{2}_dfs.jpeg".format(start_year,
end_year,
season_indicator,
dfs_var_name))
basemap = nemo_commons.get_default_basemap_for_glk(dfs_lons, dfs_lats, resolution="l")
x, y = basemap(dfs_lons, dfs_lats)
coords_and_basemap = {
"basemap": basemap, "x": x, "y": y
}
plot_errors_in_one_figure(season_to_err, fig_path=fig_path, **coords_and_basemap)
def __init__(self, path="data/swe_ross_brown/swe.nc", var_name=""):
self.lons2d, self.lats2d = None, None
self.times = None
self.var_data = None
BaseDataManager.__init__(self)
CRUDataManager.__init__(self, path=path, var_name=var_name)
print(list(self.nc_dataset.variables.keys()))
def __init__(self, path="data/swe_ross_brown/swe.nc", var_name=""):
self.lons2d, self.lats2d = None, None
self.times = None
self.var_data = None
BaseDataManager.__init__(self)
CRUDataManager.__init__(self, path=path, var_name=var_name)
print(list(self.nc_dataset.variables.keys()))
def get_cru_obs_mean_fields(target_lon_2d,
target_lat_2d,
nneighbours=1,
months=None):
tmpCruDataManager = CRUDataManager(var_name="tmp", lazy=True)
preCruDataManager = CRUDataManager(
path="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.pre.dat.nc",
var_name="pre",
lazy=True)
#get mean annual temperature from CRU dataset
tmpCru = tmpCruDataManager.get_mean(start_year=start_year,
end_year=end_year,
months=months)
#same thing for the precip
preCru = preCruDataManager.get_mean(start_year=start_year,
end_year=end_year,
months=months) ##pre is in mm/month
#convert to mm/day
preCru *= 12.0 / 365.25
tmpCruI = tmpCruDataManager.interpolate_data_to(tmpCru,
target_lon_2d,
target_lat_2d,
nneighbours=nneighbours)
preCruI = preCruDataManager.interpolate_data_to(preCru,
target_lon_2d,
target_lat_2d,
nneighbours=nneighbours)
return tmpCruI, preCruI
def get_seasonal_clim_obs_data(rconfig=None, vname="TT", bmp_info=None, season_to_months=None, obs_path=None):
# Number of points for aggregation
"""
return aggregated BasemapInfo object corresponding to the CRU resolution
:param rconfig:
:param vname:
:param bmp_info: BasemapInfo object for the model field (will be upscaled to the CRU resolution)
:param season_to_months:
"""
nx_agg = 5
ny_agg = 5
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rconfig.data_path)
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
if obs_path is None:
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=rconfig.start_year,
end_year=rconfig.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")
return bmp_info_agg, seasonal_clim_fields_obs_interp
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 main():
# seasons = OrderedDict([
# ("DJF", MonthPeriod(12, 3)),
# ("MAM", MonthPeriod(3, 3))
# ])
season_to_month_period = OrderedDict([
("DJF", MonthPeriod(12, 3))
])
start_year = 1980
end_year = 1982
vname_to_path = {
"pre": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
"tmp": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc"
}
for vname, path in vname_to_path.items():
manager = CRUDataManager(path=path, var_name=vname)
res = manager.get_seasonal_means_with_ttest_stats(
season_to_monthperiod=season_to_month_period, start_year=start_year, end_year=end_year)
plt.figure()
mean_field, std_field, nobs = res["DJF"]
im = plt.pcolormesh(mean_field.T)
plt.colorbar(im)
plt.show()
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 get_cru_obs_mean_fields(target_lon_2d, target_lat_2d, nneighbours = 1, months = None):
tmpCruDataManager = CRUDataManager(var_name="tmp", lazy = True)
preCruDataManager = CRUDataManager(path = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.pre.dat.nc", var_name="pre", lazy = True)
#get mean annual temperature from CRU dataset
tmpCru = tmpCruDataManager.get_mean(start_year=start_year, end_year = end_year, months = months)
#same thing for the precip
preCru = preCruDataManager.get_mean(start_year=start_year, end_year = end_year, months = months) ##pre is in mm/month
#convert to mm/day
preCru *= 12.0 / 365.25
tmpCruI = tmpCruDataManager.interpolate_data_to(tmpCru, target_lon_2d, target_lat_2d, nneighbours=nneighbours)
preCruI = preCruDataManager.interpolate_data_to(preCru, target_lon_2d, target_lat_2d, nneighbours=nneighbours)
return tmpCruI, preCruI
def validate_using_monthly_diagnostics():
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40", "MPI", "CanESM"]
simname_to_path = {
"ERA40":
"/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1_dm",
"MPI":
"/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_MPI_B1_dm",
"CanESM":
"/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_CanESM_B1_dm"
}
coord_file = os.path.join(
sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(
resolution="c",
file_path=coord_file,
llcrnrlat=45.0,
llcrnrlon=-145,
urcrnrlon=-20,
urcrnrlat=74,
anchor="W")
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
obs_manager = CRUDataManager()
obs = obs_manager.get_mean(start_year, end_year, months=[6, 7, 8])
obs = obs_manager.interpolate_data_to(obs, lons2d, lats2d, nneighbours=1)
x, y = basemap(lons2d, lats2d)
#x = (x[1:,1:] + x[:-1, :-1]) /2.0
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 2)
#plot_utils.apply_plot_params(width_pt=None, width_cm=35,height_cm=55, font_size=35)
fig = plt.figure()
assert isinstance(fig, Figure)
cmap = my_colormaps.get_red_blue_colormap(ncolors=10, reversed=True)
gs = gridspec.GridSpec(3,
2,
width_ratios=[1, 0.1],
hspace=0,
wspace=0,
left=0.05,
bottom=0.01,
top=0.95)
all_axes = []
all_img = []
i = 0
for name in sim_names:
path = simname_to_path[name]
dm = CRCMDataManager(data_folder=path)
mod = dm.get_mean_over_months_of_2d_var(start_year,
end_year,
months=[6, 7, 8],
var_name="TT",
level=1,
level_kind=level_kinds.HYBRID)
delta = mod - obs
ax = fig.add_subplot(gs[i, 0])
assert isinstance(ax, Axes)
delta = np.ma.masked_where(mask_cond, delta)
img = basemap.pcolormesh(x, y, delta, cmap=cmap, vmin=-5.0, vmax=5.0)
if not i:
ax.set_title("T2m, Mod - Obs, ({0} - {1}), JJA \n".format(
start_year, end_year))
i += 1
#ax.set_ylabel(name)
all_axes.append(ax)
all_img.append(img)
i = 0
axs_to_hide = []
#zones and coastlines
for the_ax, the_img in zip(all_axes, all_img):
# divider = make_axes_locatable(the_ax)
# cax = divider.append_axes("right", "5%", pad="3%")
#cax.set_title("%\n")
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax=the_ax, linewidth=0.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice",
name="zone",
ax=the_ax,
linewidth=1.5,
drawbounds=False)
for nshape, seg in enumerate(basemap.zone):
if basemap.zone_info[nshape]["EXTENT"] != "C": continue
poly = mpl.patches.Polygon(seg,
edgecolor="k",
facecolor="none",
zorder=10,
lw=1.5)
the_ax.add_patch(poly)
i += 1
cax = fig.add_subplot(gs[:, 1])
cax.set_anchor("W")
cax.set_aspect(30)
formatter = FuncFormatter(lambda x, pos: "{0: <6}".format(str(x)))
cb = fig.colorbar(all_img[0],
ax=cax,
cax=cax,
extend="both",
format=formatter)
cax.set_title("$^{\\circ} {\\rm C}$")
#fig.tight_layout(h_pad=0)
# for the_ax in axs_to_hide:
# the_ax.set_visible(False)
fig.savefig("tmp_validation_era_mpi_canesm.png")
def compare_for_season(start_year=1958,
end_year=1974,
the_months=None,
period_str="djf"):
"""
Compare CRU, ERA40-driven and GCM-driven s
"""
#b, lons2d, lats2d = draw_regions.get_basemap_and_coords(llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-10)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
lons2d[lons2d > 180] -= 360
x, y = b(lons2d, lats2d)
cru = CRUDataManager()
cru_data = cru.get_mean(start_year, end_year, months=the_months)
cru_data_interp = cru.interpolate_data_to(cru_data, lons2d, lats2d)
temp_levels = np.arange(-40, 40, 5)
diff_levels = np.arange(-10, 12, 2)
gs = gridspec.GridSpec(3, 2)
#plot_utils.apply_plot_params(width_pt=None, height_cm=20, width_cm=20, font_size=12)
fig = plt.figure()
coast_line_width = 0.25
axes_list = []
#plot CRU data
ax = fig.add_subplot(gs[0, :])
axes_list.append(ax)
cru_data_interp = maskoceans(lons2d, lats2d, cru_data_interp)
img = b.contourf(x, y, cru_data_interp, ax=ax, levels=temp_levels)
ax.set_title("CRU")
plot_utils.draw_colorbar(fig, img, ax=ax)
#era40 driven
file_path = None
era40_folder = "data/CORDEX/na/era40_1"
file_prefix = "dm"
for file_name in os.listdir(era40_folder):
if period_str.upper() in file_name and file_name.startswith(
file_prefix):
file_path = os.path.join(era40_folder, file_name)
break
#get the temperature
rpn_obj = RPN(file_path)
t2m_era40 = rpn_obj.get_first_record_for_name_and_level(
varname="TT", level=1, level_kind=level_kinds.HYBRID)
t2m_era40 = maskoceans(lons2d, lats2d, t2m_era40)
ax = fig.add_subplot(gs[1, 0])
axes_list.append(ax)
img = b.contourf(x, y, t2m_era40, ax=ax, levels=temp_levels)
ax.set_title("ERA40 driven 1 (1958-1961)")
plot_utils.draw_colorbar(fig, img, ax=ax)
rpn_obj.close()
#era40 - cru
ax = fig.add_subplot(gs[1, 1])
axes_list.append(ax)
img = b.contourf(x,
y,
t2m_era40 - cru_data_interp,
ax=ax,
levels=diff_levels)
ax.set_title("ERA40 driven 1 - CRU")
plot_utils.draw_colorbar(fig, img, ax=ax)
plot_e2_data = False
if plot_e2_data:
##get and plot E2 data
file_path = None
e2_folder = "data/CORDEX/na/e2"
prefix = "dm"
#get file path
for file_name in os.listdir(e2_folder):
if file_name.endswith(period_str) and file_name.startswith(prefix):
file_path = os.path.join(e2_folder, file_name)
break
pass
#get the temperature
rpn_obj = RPN(file_path)
t2m = rpn_obj.get_first_record_for_name_and_level(
varname="TT", level=1, level_kind=level_kinds.HYBRID)
t2m = maskoceans(lons2d, lats2d, t2m)
ax = fig.add_subplot(gs[2, 0])
axes_list.append(ax)
img = b.contourf(x, y, t2m, ax=ax, levels=temp_levels)
ax.set_title("E2, GCM driven")
plot_utils.draw_colorbar(fig, img, ax=ax)
#e2 - cru
ax = fig.add_subplot(gs[2, 1])
axes_list.append(ax)
img = b.contourf(x,
y,
t2m - cru_data_interp,
ax=ax,
levels=diff_levels)
ax.set_title("E2, GCM driven - CRU")
plot_utils.draw_colorbar(fig, img, ax=ax)
####Draw common elements
pf_kinds = draw_regions.get_permafrost_mask(lons2d, lats2d)
for the_ax in axes_list:
b.drawcoastlines(ax=the_ax, linewidth=coast_line_width)
b.contour(x, y, pf_kinds, ax=the_ax, colors="k")
gs.tight_layout(fig, h_pad=5, w_pad=5, pad=2)
fig.suptitle(period_str.upper(), y=0.03, x=0.5)
fig.savefig("temperature_validation_{0}.png".format(period_str))
fig = plt.figure()
ax = plt.gca()
img = b.contourf(x,
y,
t2m_era40 - cru_data_interp,
ax=ax,
levels=diff_levels)
ax.set_title("ERA40 driven 1 - CRU")
plot_utils.draw_colorbar(fig, img, ax=ax)
b.drawcoastlines(ax=ax, linewidth=coast_line_width)
b.contour(x, y, pf_kinds, ax=ax, colors="k")
fig.savefig("temperature_diff_{0}.png".format(period_str))
pass
def plot_seasonal_2m_temp_cru(samples_folder, seasons=commons.default_seasons):
vname = "tmp"
file_prefix = "dm"
plot_units = "$^\circ$C"
mult_coeff = 1
add_offset = 0
out_dx = 0.5
level = 1 # in hybrid coords
img_file_name = "CRU_{}.png".format(vname)
long_name = "CRU: 2m temperature, {}".format(plot_units)
# get data from cru files
data_manager = CRUDataManager(path="/RESCUE/skynet3_rech1/huziy/CRU/cru_ts3.23.2011.2014.tmp.dat.nc",
var_name=vname,
lazy=True)
season_to_mean = data_manager.get_seasonal_means(season_name_to_months=seasons, start_year=2013, end_year=2013)
lons2d, lats2d = data_manager.lons2d, data_manager.lats2d
img_folder = samples_folder.joinpath("images/seasonal")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath(img_file_name)
plot_utils.apply_plot_params(width_cm=25, height_cm=15, font_size=18)
fig = plt.figure()
ncols = 2
gs = GridSpec(len(season_to_mean) // ncols + int(not (len(season_to_mean) % ncols == 0)), ncols, wspace=0, hspace=0)
xx, yy = None, None
bmp = Basemap(projection="robin", lon_0=0)
cmap = plt.get_cmap('bwr')
clevs = np.arange(-30, 32, 2)
for i, (sname, field) in enumerate(season_to_mean.items()):
row = i // ncols
col = i % ncols
ax = fig.add_subplot(gs[row, col])
lons, lats, data_out = commons.interpolate_to_uniform_global_grid(field, lons_in=lons2d, lats_in=lats2d, out_dx=out_dx)
if xx is None:
xx, yy = bmp(lons, lats)
cs = bmp.contourf(xx, yy, data_out * mult_coeff, clevs, cmap=cmap, extend="both")
# save color levels for next subplots
clevs = cs.levels
print(np.max(data_out * mult_coeff))
ax.set_title(sname)
cb = plt.colorbar(cs, ax=ax)
if not (row == 0 and col == ncols - 1):
# cb.ax.set_title(plot_units)
cb.ax.set_visible(False)
bmp.drawcoastlines(ax=ax, linewidth=LINEWIDTH)
fig.suptitle(long_name)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def validate_thawing_index():
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40", "MPI", "CanESM"]
fold = "/home/samira/skynet/DailyTempData"
simname_to_path = {
"ERA40": os.path.join(fold, "TempERA40_b1_1981-2008.nc"),
"MPI": os.path.join(fold, "TempMPI1981-2010.nc"),
"CanESM": os.path.join(fold, "TempCanESM1981-2010.nc"),
}
coord_file = os.path.join(
sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(
resolution="c",
file_path=coord_file,
llcrnrlat=40.0,
llcrnrlon=-145,
urcrnrlon=-20,
urcrnrlat=74)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
om = CRUDataManager()
clim = om.get_daily_climatology(start_year, end_year)
obs = om.get_thawing_index_from_climatology(clim)
obs = om.interpolate_data_to(
obs, lons2d, lats2d, nneighbours=1) #interpolatee to the model grid
x, y = basemap(lons2d, lats2d)
#x = (x[1:,1:] + x[:-1, :-1]) /2.0
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
#plot_utils.apply_plot_params(width_pt=None, width_cm=35,height_cm=55, font_size=35)
fig = plt.figure()
assert isinstance(fig, Figure)
cmap = my_colormaps.get_red_blue_colormap(ncolors=10, reversed=True)
gs = gridspec.GridSpec(3, 1)
all_axes = []
all_img = []
i = 0
for name in sim_names:
path = simname_to_path[name]
ds = Dataset(path)
data_mod = ds.variables["air"][:]
mod = _get_thawing_index()
delta = mod - obs
ax = fig.add_subplot(gs[i, 0])
assert isinstance(ax, Axes)
delta = np.ma.masked_where(mask_cond, delta)
img = basemap.pcolormesh(x, y, delta, cmap=cmap, vmin=None, vmax=None)
if not i:
ax.set_title("Thawing index, Mod - Obs, ({0} - {1}) \n".format(
start_year, end_year))
i += 1
ax.set_ylabel(name)
all_axes.append(ax)
all_img.append(img)
i = 0
axs_to_hide = []
#zones and coastlines
for the_ax, the_img in zip(all_axes, all_img):
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax=cax, extend="both")
cax.set_title("$^{\\circ} {\\rm C}$\n")
#cax.set_title("%\n")
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax=the_ax, linewidth=1.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice",
name="zone",
ax=the_ax,
linewidth=3)
if i != 1:
axs_to_hide.append(cax)
i += 1
fig.tight_layout()
for the_ax in axs_to_hide:
the_ax.set_visible(False)
fig.savefig("tmp_validation_era_mpi_canesm.png")
pass
def plot_seasonal_2m_temp_cru(samples_folder, seasons=commons.default_seasons):
vname = "tmp"
file_prefix = "dm"
plot_units = "$^\circ$C"
mult_coeff = 1
add_offset = 0
out_dx = 0.5
level = 1 # in hybrid coords
img_file_name = "CRU_{}.png".format(vname)
long_name = "CRU: 2m temperature, {}".format(plot_units)
# get data from cru files
data_manager = CRUDataManager(
path="/RESCUE/skynet3_rech1/huziy/CRU/cru_ts3.23.2011.2014.tmp.dat.nc",
var_name=vname,
lazy=True)
season_to_mean = data_manager.get_seasonal_means(
season_name_to_months=seasons, start_year=2013, end_year=2013)
lons2d, lats2d = data_manager.lons2d, data_manager.lats2d
img_folder = samples_folder.joinpath("images/seasonal")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath(img_file_name)
plot_utils.apply_plot_params(width_cm=25, height_cm=15, font_size=18)
fig = plt.figure()
ncols = 2
gs = GridSpec(len(season_to_mean) // ncols +
int(not (len(season_to_mean) % ncols == 0)),
ncols,
wspace=0,
hspace=0)
xx, yy = None, None
bmp = Basemap(projection="robin", lon_0=0)
cmap = plt.get_cmap('bwr')
clevs = np.arange(-30, 32, 2)
for i, (sname, field) in enumerate(season_to_mean.items()):
row = i // ncols
col = i % ncols
ax = fig.add_subplot(gs[row, col])
lons, lats, data_out = commons.interpolate_to_uniform_global_grid(
field, lons_in=lons2d, lats_in=lats2d, out_dx=out_dx)
if xx is None:
xx, yy = bmp(lons, lats)
cs = bmp.contourf(xx,
yy,
data_out * mult_coeff,
clevs,
cmap=cmap,
extend="both")
# save color levels for next subplots
clevs = cs.levels
print(np.max(data_out * mult_coeff))
ax.set_title(sname)
cb = plt.colorbar(cs, ax=ax)
if not (row == 0 and col == ncols - 1):
# cb.ax.set_title(plot_units)
cb.ax.set_visible(False)
bmp.drawcoastlines(ax=ax, linewidth=LINEWIDTH)
fig.suptitle(long_name)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main(
dfs_var_name="t2",
cru_var_name="tmp",
dfs_folder="/home/huziy/skynet3_rech1/NEMO_OFFICIAL/DFS5.2_interpolated",
cru_file="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc"):
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
#year range is inclusive [start_year, end_year]
start_year = 1981
end_year = 2009
season_name_to_months = OrderedDict([("Winter", (1, 2, 12)),
("Spring", list(range(3, 6))),
("Summer", list(range(6, 9))),
("Fall", list(range(9, 12)))])
cru_t_manager = CRUDataManager(var_name=cru_var_name, path=cru_file)
cru_lons, cru_lats = cru_t_manager.lons2d, cru_t_manager.lats2d
#get seasonal means (CRU)
season_to_mean_cru = cru_t_manager.get_seasonal_means(
season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
#get seasonal means Drakkar
dfs_manager = DFSDataManager(folder_path=dfs_folder, var_name=dfs_var_name)
season_to_mean_dfs = dfs_manager.get_seasonal_means(
season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
dfs_lons, dfs_lats = dfs_manager.get_lons_and_lats_2d()
xt, yt, zt = lat_lon.lon_lat_to_cartesian(dfs_lons.flatten(),
dfs_lats.flatten())
xs, ys, zs = lat_lon.lon_lat_to_cartesian(cru_lons.flatten(),
cru_lats.flatten())
ktree = cKDTree(data=list(zip(xs, ys, zs)))
dists, inds = ktree.query(list(zip(xt, yt, zt)))
season_to_err = OrderedDict()
for k in season_to_mean_dfs:
interpolated_cru = season_to_mean_cru[k].flatten()[inds].reshape(
dfs_lons.shape)
if dfs_var_name.lower() == "t2":
#interpolated_cru += 273.15
season_to_mean_dfs[k] -= 273.15
elif dfs_var_name.lower() == "precip": # precipitation in mm/day
season_to_mean_dfs[k] *= 24 * 60 * 60
season_to_err[k] = season_to_mean_dfs[k] #- interpolated_cru
season_indicator = "-".join(sorted(season_to_err.keys()))
fig_path = os.path.join(
NEMO_IMAGES_DIR,
"{3}_errors_{0}-{1}_{2}_dfs.jpeg".format(start_year, end_year,
season_indicator,
dfs_var_name))
basemap = nemo_commons.get_default_basemap_for_glk(dfs_lons,
dfs_lats,
resolution="l")
x, y = basemap(dfs_lons, dfs_lats)
coords_and_basemap = {"basemap": basemap, "x": x, "y": y}
plot_errors_in_one_figure(season_to_err,
fig_path=fig_path,
**coords_and_basemap)
def validate_daily_climatology():
"""
"""
#years are inclusive
start_year = 1979
end_year = 1988
#sim_name_list = ["crcm5-r", "crcm5-hcd-r", "crcm5-hcd-rl"]
sim_name_list = ["crcm5-hcd-rl", "crcm5-hcd-rl-intfl"]
rpn_folder_paths = [
"/home/huziy/skynet3_rech1/from_guillimin/new_outputs/quebec_0.1_{0}_spinup".format(sim_name_list[0]),
"/home/huziy/skynet3_rech1/from_guillimin/new_outputs/quebec_0.1_{0}_spinup2/Samples_all_in_one_folder".format(
sim_name_list[1])
]
nc_db_folder = "/home/huziy/skynet3_rech1/crcm_data_ncdb"
#select stations
selected_ids = None
selected_ids = ["092715", "080101", "074903", "050304", "080104", "081007", "061905",
"041903", "040830", "093806", "090613", "081002", "093801", "080718"]
selected_ids = ["074903", ]
start_date = datetime(start_year, 1, 1)
end_date = datetime(end_year, 12, 31)
selected_ids = None
stations = cehq_station.read_station_data(selected_ids=selected_ids,
start_date=start_date, end_date=end_date
)
stations_hydat = cehq_station.read_hydat_station_data(folder_path="/home/huziy/skynet3_rech1/HYDAT",
start_date=start_date, end_date=end_date)
stations.extend(stations_hydat)
varname = "STFL"
sim_name_to_manager = {}
sim_name_to_station_to_model_point = {}
day_stamps = Station.get_stamp_days(2001)
sweManager = SweDataManager(var_name="SWE")
cruTempManager = CRUDataManager(lazy=True)
cruPreManager = CRUDataManager(var_name="pre", lazy=True,
path="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.pre.dat.nc")
#common lake fractions when comparing simulations on the same grid
all_model_points = []
cell_manager = None
for sim_name, rpn_folder in zip(sim_name_list, rpn_folder_paths):
dmManager = Crcm5ModelDataManager(samples_folder_path=rpn_folder, file_name_prefix="dm",
all_files_in_samples_folder=True, need_cell_manager=cell_manager is None)
#here using the fact that all the simulations are on the same grid
if cell_manager is None:
cell_manager = dmManager.cell_manager
else:
dmManager.cell_manager = cell_manager
#determine comon lake fractions, so it is not taken from the trivial case lf = 0, but note
#this has only sense when all the simulations were performed on the same grid
sim_name_to_manager[sim_name] = dmManager
nc_sim_folder = os.path.join(nc_db_folder, sim_name)
nc_path = os.path.join(nc_sim_folder, "{0}_all.nc4".format(varname))
#In general there are several model points corresponding to a given station
st_to_mp = dmManager.get_model_points_for_stations(stations, sim_name=sim_name,
nc_path=nc_path,
nc_sim_folder=nc_sim_folder,
set_data_to_model_points=True)
print("got model points for stations")
sim_name_to_station_to_model_point[sim_name] = st_to_mp
#save model points to a list of all points
for s, mps in st_to_mp.items():
assert isinstance(s, Station)
for mp in mps:
assert isinstance(mp, ModelPoint)
#calculate upstream swe if needed
if s.mean_swe_upstream_daily_clim is None:
s.mean_swe_upstream_daily_clim = sweManager.get_mean_upstream_timeseries_daily(mp, dmManager,
stamp_dates=day_stamps)
#These are taken from CRU dataset, only monthly data are available
s.mean_temp_upstream_monthly_clim = cruTempManager.get_mean_upstream_timeseries_monthly(mp,
dmManager)
s.mean_prec_upstream_monthly_clim = cruPreManager.get_mean_upstream_timeseries_monthly(mp,
dmManager)
print("Calculated observed upstream mean values...")
all_model_points.extend(mps)
print("imported input data successfully, plotting ...")
#for tests
#test(sim_name_to_station_to_model_point)
#select only stations which have corresponding model points
stations = list(sim_name_to_station_to_model_point[sim_name_list[0]].keys())
from matplotlib.backends.backend_pdf import PdfPages
for s in stations:
years = s.get_list_of_complete_years()
if len(years) < 6: continue #skip stations with less than 6 continuous years of data
pp = PdfPages("nc_diagnose_{0}.pdf".format(s.id))
#plot hydrographs
fig = plt.figure()
gs = gridspec.GridSpec(3, 3, left=0.05, hspace=0.3, wspace=0.2)
ax_stfl = fig.add_subplot(gs[0, 0])
labels, handles = plot_hydrographs(ax_stfl, s, sim_name_to_station_to_model_point,
day_stamps=day_stamps, sim_names=sim_name_list
)
plt.setp(ax_stfl.get_xticklabels(), visible=False) #do not show ticklabels for upper rows
fig.legend(handles, labels, "lower right")
#plot swe 1d compare with obs
ax_swe = fig.add_subplot(gs[1, 0], sharex=ax_stfl)
plot_swe_1d_compare_with_obs(ax_swe, s, sim_name_to_station_to_model_point,
day_stamps=day_stamps, sim_names=sim_name_list)
#plot mean temp 1d compare with obs -- here plot biases directly...??
ax_temp = fig.add_subplot(gs[0, 2])
plot_temp_1d_compare_with_obs(ax_temp, s, sim_name_to_station_to_model_point, sim_names=sim_name_list)
plt.setp(ax_temp.get_xticklabels(), visible=False) #do not show ticklabels for upper rows
#plot mean precip 1d compare with obs -- here plot biases directly...??
ax = fig.add_subplot(gs[1, 2], sharex=ax_temp)
plot_precip_1d_compare_with_obs(ax, s, sim_name_to_station_to_model_point, sim_names=sim_name_list)
#plot mean Surface and subsurface runoff
ax = fig.add_subplot(gs[0, 1], sharex=ax_stfl)
plot_surf_runoff(ax, s, sim_name_to_station_to_model_point, sim_names=sim_name_list)
plt.setp(ax.get_xticklabels(), visible=False) #do not show ticklabels for upper rows
ax = fig.add_subplot(gs[1, 1], sharex=ax_stfl)
plot_subsurf_runoff(ax, s, sim_name_to_station_to_model_point, sim_names=sim_name_list)
plt.setp(ax.get_xticklabels(), visible=False) #do not show ticklabels for upper rows
ax = fig.add_subplot(gs[2, 1], sharex=ax_stfl)
plot_total_runoff(ax, s, sim_name_to_station_to_model_point, sim_names=sim_name_list)
pp.savefig()
#plot flow direction and basin boundaries
fig = plt.figure()
gs = gridspec.GridSpec(1, 2, right=0.99, bottom=0.001)
ax = fig.add_subplot(gs[0, 1])
plot_flow_directions_and_basin_boundaries(ax, s, sim_name_to_station_to_model_point,
sim_name_to_manager=sim_name_to_manager)
pp.savefig()
#plot 2d correlation between wind speed and measured streamflow at the station
pp.close()
def validate_thawing_index():
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40","MPI","CanESM"]
fold = "/home/samira/skynet/DailyTempData"
simname_to_path = {
"ERA40": os.path.join(fold, "TempERA40_b1_1981-2008.nc"),
"MPI": os.path.join(fold, "TempMPI1981-2010.nc"),
"CanESM":os.path.join(fold, "TempCanESM1981-2010.nc"),
}
coord_file = os.path.join(sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
file_path = coord_file, llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
om = CRUDataManager()
clim = om.get_daily_climatology(start_year, end_year)
obs = om.get_thawing_index_from_climatology(clim)
obs = om.interpolate_data_to(obs, lons2d, lats2d, nneighbours=1) #interpolatee to the model grid
x, y = basemap(lons2d, lats2d)
#x = (x[1:,1:] + x[:-1, :-1]) /2.0
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 3)
#plot_utils.apply_plot_params(width_pt=None, width_cm=35,height_cm=55, font_size=35)
fig = plt.figure()
assert isinstance(fig, Figure)
cmap = my_colormaps.get_red_blue_colormap(ncolors=10, reversed=True)
gs = gridspec.GridSpec(3,1)
all_axes = []
all_img = []
i = 0
for name in sim_names:
path = simname_to_path[name]
ds = Dataset(path)
data_mod = ds.variables["air"][:]
mod = _get_thawing_index()
delta = mod - obs
ax = fig.add_subplot(gs[i,0])
assert isinstance(ax, Axes)
delta = np.ma.masked_where(mask_cond, delta)
img = basemap.pcolormesh(x, y, delta, cmap = cmap, vmin=None, vmax = None)
if not i:
ax.set_title("Thawing index, Mod - Obs, ({0} - {1}) \n".format(start_year, end_year))
i += 1
ax.set_ylabel(name)
all_axes.append(ax)
all_img.append(img)
i = 0
axs_to_hide = []
#zones and coastlines
for the_ax, the_img in zip(all_axes, all_img):
divider = make_axes_locatable(the_ax)
cax = divider.append_axes("right", "5%", pad="3%")
cb = fig.colorbar(the_img, cax = cax, extend = "both")
cax.set_title("$^{\\circ} {\\rm C}$\n")
#cax.set_title("%\n")
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax = the_ax, linewidth=1.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
ax=the_ax, linewidth=3)
if i != 1:
axs_to_hide.append(cax)
i += 1
fig.tight_layout()
for the_ax in axs_to_hide:
the_ax.set_visible(False)
fig.savefig("tmp_validation_era_mpi_canesm.png")
pass
def validate_using_monthly_diagnostics():
start_year = 1980
end_year = 1996
sim_data_folder = "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1"
sim_names = ["ERA40","MPI","CanESM"]
simname_to_path = {
"ERA40": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/era40_driven_b1_dm",
"MPI": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_MPI_B1_dm",
"CanESM": "/home/huziy/skynet1_rech3/cordex/CORDEX_DIAG/NorthAmerica_0.44deg_CanESM_B1_dm"
}
coord_file = os.path.join(sim_data_folder, "pmNorthAmerica_0.44deg_ERA40-Int_B1_200812_moyenne")
basemap, lons2d, lats2d = draw_regions.get_basemap_and_coords(resolution="c",
file_path = coord_file, llcrnrlat=45.0, llcrnrlon=-145, urcrnrlon=-20, urcrnrlat=74,
anchor = "W"
)
assert isinstance(basemap, Basemap)
lons2d[lons2d > 180] -= 360
obs_manager = CRUDataManager()
obs = obs_manager.get_mean(start_year, end_year, months=[6,7,8])
obs = obs_manager.interpolate_data_to(obs, lons2d, lats2d, nneighbours=1)
x, y = basemap(lons2d, lats2d)
#x = (x[1:,1:] + x[:-1, :-1]) /2.0
permafrost_mask = draw_regions.get_permafrost_mask(lons2d, lats2d)
mask_cond = (permafrost_mask <= 0) | (permafrost_mask >= 2)
#plot_utils.apply_plot_params(width_pt=None, width_cm=35,height_cm=55, font_size=35)
fig = plt.figure()
assert isinstance(fig, Figure)
cmap = my_colormaps.get_red_blue_colormap(ncolors=10, reversed=True)
gs = gridspec.GridSpec(3,2, width_ratios=[1,0.1], hspace=0, wspace=0,
left=0.05, bottom = 0.01, top=0.95)
all_axes = []
all_img = []
i = 0
for name in sim_names:
path = simname_to_path[name]
dm = CRCMDataManager(data_folder=path)
mod = dm.get_mean_over_months_of_2d_var(start_year, end_year, months = [6,7,8],
var_name="TT", level=1, level_kind=level_kinds.HYBRID)
delta = mod - obs
ax = fig.add_subplot(gs[i,0])
assert isinstance(ax, Axes)
delta = np.ma.masked_where(mask_cond, delta)
img = basemap.pcolormesh(x, y, delta, cmap = cmap, vmin=-5.0, vmax = 5.0)
if not i:
ax.set_title("T2m, Mod - Obs, ({0} - {1}), JJA \n".format(start_year, end_year))
i += 1
#ax.set_ylabel(name)
all_axes.append(ax)
all_img.append(img)
i = 0
axs_to_hide = []
#zones and coastlines
for the_ax, the_img in zip(all_axes, all_img):
# divider = make_axes_locatable(the_ax)
# cax = divider.append_axes("right", "5%", pad="3%")
#cax.set_title("%\n")
assert isinstance(the_ax, Axes)
basemap.drawcoastlines(ax = the_ax, linewidth=0.5)
basemap.readshapefile("data/pf_4/permafrost8_wgs84/permaice", name="zone",
ax=the_ax, linewidth=1.5, drawbounds=False)
for nshape,seg in enumerate(basemap.zone):
if basemap.zone_info[nshape]["EXTENT"] != "C": continue
poly = mpl.patches.Polygon(seg,edgecolor = "k", facecolor="none", zorder = 10, lw = 1.5)
the_ax.add_patch(poly)
i += 1
cax = fig.add_subplot(gs[:,1])
cax.set_anchor("W")
cax.set_aspect(30)
formatter = FuncFormatter(
lambda x, pos: "{0: <6}".format(str(x))
)
cb = fig.colorbar(all_img[0], ax = cax, cax = cax,
extend = "both", format = formatter)
cax.set_title("$^{\\circ} {\\rm C}$")
#fig.tight_layout(h_pad=0)
# for the_ax in axs_to_hide:
# the_ax.set_visible(False)
fig.savefig("tmp_validation_era_mpi_canesm.png")
def compare_for_season( start_year = 1958,
end_year = 1974,
the_months = None,
period_str = "djf"):
"""
Compare CRU, ERA40-driven and GCM-driven s
"""
#b, lons2d, lats2d = draw_regions.get_basemap_and_coords(llcrnrlat=40.0, llcrnrlon=-145, urcrnrlon=-10)
b, lons2d, lats2d = draw_regions.get_basemap_and_coords()
lons2d[lons2d > 180] -= 360
x, y = b(lons2d, lats2d)
cru = CRUDataManager()
cru_data = cru.get_mean(start_year,end_year, months = the_months)
cru_data_interp = cru.interpolate_data_to(cru_data, lons2d, lats2d)
temp_levels = np.arange(-40, 40, 5)
diff_levels = np.arange(-10, 12, 2)
gs = gridspec.GridSpec(3,2)
#plot_utils.apply_plot_params(width_pt=None, height_cm=20, width_cm=20, font_size=12)
fig = plt.figure()
coast_line_width = 0.25
axes_list = []
#plot CRU data
ax = fig.add_subplot(gs[0,:])
axes_list.append(ax)
cru_data_interp = maskoceans(lons2d, lats2d, cru_data_interp)
img = b.contourf(x, y, cru_data_interp, ax = ax, levels = temp_levels)
ax.set_title("CRU")
plot_utils.draw_colorbar(fig, img, ax = ax)
#era40 driven
file_path = None
era40_folder = "data/CORDEX/na/era40_1"
file_prefix = "dm"
for file_name in os.listdir(era40_folder):
if period_str.upper() in file_name and file_name.startswith(file_prefix):
file_path = os.path.join(era40_folder, file_name)
break
#get the temperature
rpn_obj = RPN(file_path)
t2m_era40 = rpn_obj.get_first_record_for_name_and_level(varname="TT",
level=1, level_kind=level_kinds.HYBRID)
t2m_era40 = maskoceans(lons2d, lats2d, t2m_era40)
ax = fig.add_subplot(gs[1,0])
axes_list.append(ax)
img = b.contourf(x, y, t2m_era40, ax = ax, levels = temp_levels)
ax.set_title("ERA40 driven 1 (1958-1961)")
plot_utils.draw_colorbar(fig, img, ax = ax)
rpn_obj.close()
#era40 - cru
ax = fig.add_subplot(gs[1,1])
axes_list.append(ax)
img = b.contourf(x, y, t2m_era40 - cru_data_interp, ax = ax, levels = diff_levels)
ax.set_title("ERA40 driven 1 - CRU")
plot_utils.draw_colorbar(fig, img, ax = ax)
plot_e2_data = False
if plot_e2_data:
##get and plot E2 data
file_path = None
e2_folder = "data/CORDEX/na/e2"
prefix = "dm"
#get file path
for file_name in os.listdir(e2_folder):
if file_name.endswith(period_str) and file_name.startswith(prefix):
file_path = os.path.join(e2_folder, file_name)
break
pass
#get the temperature
rpn_obj = RPN(file_path)
t2m = rpn_obj.get_first_record_for_name_and_level(varname="TT",
level=1, level_kind=level_kinds.HYBRID)
t2m = maskoceans(lons2d, lats2d, t2m)
ax = fig.add_subplot(gs[2,0])
axes_list.append(ax)
img = b.contourf(x, y, t2m, ax = ax, levels = temp_levels)
ax.set_title("E2, GCM driven")
plot_utils.draw_colorbar(fig, img, ax = ax)
#e2 - cru
ax = fig.add_subplot(gs[2,1])
axes_list.append(ax)
img = b.contourf(x, y, t2m - cru_data_interp, ax = ax, levels = diff_levels)
ax.set_title("E2, GCM driven - CRU")
plot_utils.draw_colorbar(fig, img, ax = ax)
####Draw common elements
pf_kinds = draw_regions.get_permafrost_mask(lons2d, lats2d)
for the_ax in axes_list:
b.drawcoastlines(ax = the_ax, linewidth = coast_line_width)
b.contour(x, y, pf_kinds, ax = the_ax, colors = "k")
gs.tight_layout(fig, h_pad = 5, w_pad = 5, pad=2)
fig.suptitle(period_str.upper(), y = 0.03, x = 0.5)
fig.savefig("temperature_validation_{0}.png".format(period_str))
fig = plt.figure()
ax = plt.gca()
img = b.contourf(x, y, t2m_era40 - cru_data_interp, ax = ax, levels = diff_levels)
ax.set_title("ERA40 driven 1 - CRU")
plot_utils.draw_colorbar(fig, img, ax = ax)
b.drawcoastlines(ax = ax, linewidth = coast_line_width)
b.contour(x, y, pf_kinds, ax = ax, colors = "k")
fig.savefig("temperature_diff_{0}.png".format(period_str))
pass
def plot_total_precip_and_temp_re_1d(ax_pr,
ax_temp,
crcm5_manager,
rot_latlon_projection,
areas2d,
model_data,
mask=None):
"""
plot relative error of total precipitation in time
"""
assert isinstance(crcm5_manager, Crcm5ModelDataManager)
assert isinstance(ax_pr, Axes)
if mask is None:
i_model0, j_model0 = model_data.metadata["ix"], model_data.metadata[
"jy"]
mask = crcm5_manager.get_mask_for_cells_upstream(i_model0, j_model0)
#print model_data.time[0], model_data.time[-1]
#####Precipitation
ts_prec_mod, dt_mod = crcm5_manager.get_monthly_sums_over_points(
mask,
"PR",
areas2d=areas2d,
start_date=model_data.time[0],
end_date=model_data.time[-1])
cruManager = CRUDataManager(
path="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.pre.dat.nc",
var_name="pre")
ts_prec_obs = cruManager.get_monthly_timeseries_using_mask(
mask,
crcm5_manager.lons2D,
crcm5_manager.lats2D,
areas2d,
start_date=model_data.time[0],
end_date=model_data.time[-1])
mod = np.array(
ts_prec_mod.data) * dt_mod.seconds * 1000.0 #converting to mm/month
sta = np.array(ts_prec_obs.data) #CRU data used was initially in mm/month
#print sta
#print mod
#print mod.shape
#print sta.shape
assert len(sta) == len(mod)
ax_pr.annotate("r = {0:.2f}".format(float(np.corrcoef([mod, sta])[0, 1])),
xy=(0.7, 0.8),
xycoords="axes fraction")
ax_pr.annotate("ns = {0:.2f}".format(scores.nash_sutcliffe(mod, sta)),
xy=(0.7, 0.9),
xycoords="axes fraction")
ax_pr.plot(ts_prec_mod.time, (mod - sta) / sta, color="k", linewidth=2)
ax_pr.xaxis.set_major_formatter(DateFormatter("%y/%m"))
ax_pr.xaxis.set_major_locator(MonthLocator(bymonth=list(range(1, 13, 2))))
ax_pr.set_title("CRCM5 versus CRU")
ax_pr.set_ylabel("Total Precip (Pmod - Pobs)/Pobs")
#Temperature
ts_temp_mod = crcm5_manager.get_spatial_integral_over_mask_of_dyn_field(
mask,
areas2d,
var_name="TT",
level=1000,
level_kind=level_kinds.PRESSURE,
path_to_folder=crcm5_manager.samples_folder,
file_prefix="dp")
print("ts_temp_mod time interval: ", ts_temp_mod.time[0],
ts_temp_mod.time[-1])
ts_temp_mod = ts_temp_mod.time_slice(model_data.time[0],
model_data.time[-1])
cruManager = CRUDataManager(
path="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc",
var_name="tmp")
ts_temp_obs = cruManager.get_monthly_timeseries_using_mask(
mask,
crcm5_manager.lons2D,
crcm5_manager.lats2D,
areas2d,
start_date=model_data.time[0],
end_date=model_data.time[-1])
mod = np.array(ts_temp_mod.get_ts_of_monthly_means().data) / sum(
areas2d[mask == 1]) #converting to mm/month
sta = np.array(ts_temp_obs.data) / sum(
areas2d[mask == 1]) #CRU data used was initially in mm/month
assert len(sta) == len(mod)
ax_temp.annotate("r = {0:.2f}".format(float(np.corrcoef([mod, sta])[0,
1])),
xy=(0.7, 0.8),
xycoords="axes fraction")
ax_temp.annotate("ns = {0:.2f}".format(scores.nash_sutcliffe(mod, sta)),
xy=(0.7, 0.9),
xycoords="axes fraction")
ax_temp.plot(ts_prec_mod.time, (mod - sta), color="k", linewidth=2)
ax_temp.xaxis.set_major_formatter(DateFormatter("%y/%m"))
ax_temp.xaxis.set_major_locator(
MonthLocator(bymonth=list(range(1, 13, 2))))
ax_temp.set_title("CRCM5 versus CRU")
ax_temp.set_ylabel("Temperature, deg., Tmod - Tobs")
pass
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([
(season_key, [6, 7, 8])
])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=start_year,
end_year=end_year)
# Calculate seasonal mean for erai
flist = get_files_for_season(erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key])
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=lons_era,
lats2d=lats_era,
nneighbours=1)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([(season_key, [6, 7, 8])])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(
season_name_to_months=season_to_months, start_year=start_year, end_year=end_year
)
# Calculate seasonal mean for erai
flist = get_files_for_season(
erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key]
)
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(
cru_field, lons2d=lons_era, lats2d=lats_era, nneighbours=1
)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
def main():
img_folder = Path("nei_validation")
img_folder.mkdir(parents=True, exist_ok=True)
var_names = ["TT", "PR"]
seasons = OrderedDict([
("DJF", MonthPeriod(12, 3)),
("MAM", MonthPeriod(3, 3)),
("JJA", MonthPeriod(6, 3)),
("SON", MonthPeriod(9, 3)),
])
sim_paths = OrderedDict()
start_year = 1980
end_year = 2008
sim_paths["WC_0.44deg_default"] = Path \
("/HOME/huziy/skynet3_rech1/CRCM5_outputs/NEI/diags/NEI_WC0.44deg_default/Diagnostics")
sim_paths["WC_0.44deg_ctem+frsoil+dyngla"] = Path \
("/HOME/huziy/skynet3_rech1/CRCM5_outputs/NEI/diags/debug_NEI_WC0.44deg_Crr1/Diagnostics")
sim_paths["WC_0.11deg_ctem+frsoil+dyngla"] = Path(
"/snow3/huziy/NEI/WC/NEI_WC0.11deg_Crr1/Diagnostics")
cru_vname_to_path = {
"pre":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
"tmp":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc"
}
plot_cru_data = True
plot_model_data = True
plot_naobs_data = True
plot_daymet_data = True
plot_utils.apply_plot_params(font_size=14)
basemap_for_obs = None
# plot simulation data
for sim_label, sim_path in sim_paths.items():
manager = DiagCrcmManager(data_dir=sim_path)
# get the basemap to be reused for plotting observation data
if basemap_for_obs is None:
basemap_for_obs = manager.get_basemap(resolution="i",
area_thresh=area_thresh_km2)
if not plot_model_data:
break
for vname in var_names:
seas_to_clim = manager.get_seasonal_means_with_ttest_stats(
season_to_monthperiod=seasons,
start_year=start_year,
end_year=end_year,
vname=vname,
vertical_level=var_name_to_level[vname],
data_file_prefix=var_name_to_file_prefix[vname])
_plot_seasonal_data(
seas_data=seas_to_clim,
data_label="{}_{}-{}".format(sim_label, start_year, end_year),
img_dir=img_folder,
map=manager.get_basemap(resolution="i",
area_thresh=area_thresh_km2),
lons=manager.lons,
lats=manager.lats,
vname=vname)
assert basemap_for_obs is not None
# plot obs data
# -- CRU
for vname in var_names:
if not plot_cru_data:
break
cru_vname = var_name_to_cru_name[vname]
manager = CRUDataManager(path=cru_vname_to_path[cru_vname],
var_name=cru_vname)
seas_to_clim = manager.get_seasonal_means_with_ttest_stats(
season_to_monthperiod=seasons,
start_year=start_year,
end_year=end_year)
manager.close()
_plot_seasonal_data(seas_data=seas_to_clim,
data_label="{}_{}-{}".format(
"CRU", start_year, end_year),
img_dir=img_folder,
map=basemap_for_obs,
lons=manager.lons2d,
lats=manager.lats2d,
vname=vname,
var_name_to_mul={
"TT": 1,
"PR": 1
})
# -- NAOBS
naobs_vname_to_path = {
"TT":
"/HOME/huziy/skynet3_rech1/obs_data/anuspl_uw_0.11_wc_domain/anusplin+_interpolated_tt_pr.nc",
"PR":
"/HOME/huziy/skynet3_rech1/obs_data/anuspl_uw_0.11_wc_domain/anusplin+_interpolated_tt_pr.nc"
}
for vname in var_names:
if not plot_naobs_data:
break
manager = CRUDataManager(path=naobs_vname_to_path[vname],
var_name=vname)
seas_to_clim = manager.get_seasonal_means_with_ttest_stats(
season_to_monthperiod=seasons,
start_year=start_year,
end_year=end_year)
# mask no data points
for s, data in seas_to_clim.items():
for i in [0, 1]:
data[i] = np.ma.masked_where(manager.lats2d > 60, data[i])
data[i] = np.ma.masked_where(manager.lons2d < -150, data[i])
data[i] = maskoceans(manager.lons2d,
manager.lats2d,
datain=data[i])
_plot_seasonal_data(seas_data=seas_to_clim,
data_label="{}_{}-{}".format(
"NAOBS", start_year, end_year),
img_dir=img_folder,
map=basemap_for_obs,
lons=manager.lons2d,
lats=manager.lats2d,
vname=vname)
manager.close()
# -- daymet monthly
daymet_vname_to_path = {
"prcp":
"/HOME/data/Validation/Daymet/Monthly_means/NetCDF/daymet_v3_prcp_monttl_*_na.nc4",
"tavg":
"/HOME/huziy/skynet3_rech1/obs_data/daymet_tavg_monthly/daymet_v3_tavg_monavg_*_na_nc4classic.nc4"
}
vname_to_daymet_vname = {"PR": "prcp", "TT": "tavg"}
for vname in var_names:
if not plot_daymet_data:
break
daymet_vname = vname_to_daymet_vname[vname]
manager = HighResDataManager(path=daymet_vname_to_path[daymet_vname],
vname=daymet_vname)
seas_to_clim = manager.get_seasonal_means_with_ttest_stats_dask(
season_to_monthperiod=seasons,
start_year=start_year,
end_year=end_year,
convert_monthly_accumulators_to_daily=(vname == "PR"))
_plot_seasonal_data(seas_data=seas_to_clim,
data_label="{}_{}-{}".format(
"DAYMET", start_year, end_year),
img_dir=img_folder,
map=basemap_for_obs,
lons=manager.lons,
lats=manager.lats,
vname=vname,
var_name_to_mul={
"PR": 1,
"TT": 1
})
manager.close()
def compare_vars(vname_model="TT",
vname_obs="tmp",
r_config=None,
season_to_months=None,
obs_path=None,
nx_agg_model=5,
ny_agg_model=5,
bmp_info_agg=None,
diff_axes_list=None,
obs_axes_list=None,
model_axes_list=None,
bmp_info_model=None,
mask_shape_file=None,
nx_agg_obs=1,
ny_agg_obs=1):
"""
if obs_axes_list is not None, plot observation data in those
:param mask_shape_file:
:param bmp_info_model: basemap info native to the model
:param model_axes_list: Axes to plot model outputs
:param vname_model:
:param vname_obs:
:param r_config:
:param season_to_months:
:param obs_path:
:param nx_agg_model:
:param ny_agg_model:
:param bmp_info_agg:
:param diff_axes_list: if it is None the plots for each variable is done in separate figures
"""
if vname_obs is None:
vname_model_to_vname_obs = {"TT": "tmp", "PR": "pre"}
vname_obs = vname_model_to_vname_obs[vname_model]
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_model, nagg_y=ny_agg_model)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if vname_obs in [
"SWE",
]:
obs_manager = SweDataManager(path=obs_path, var_name=vname_obs)
elif obs_path is None:
obs_manager = CRUDataManager(var_name=vname_obs)
else:
obs_manager = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = obs_manager.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()
# Derive the mask from a shapefile if provided
if mask_shape_file is not None:
the_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=mask_shape_file)
else:
the_mask = np.zeros_like(bmp_info_agg.lons)
for season, obs_field in seasonal_clim_fields_obs.items():
obs_field = obs_manager.interpolate_data_to(obs_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=nx_agg_obs *
ny_agg_obs)
obs_field = np.ma.masked_where(the_mask > 0.5, obs_field)
seasonal_clim_fields_obs_interp[season] = obs_field
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
print("-------------var: {} (PE with CRU)---------------------".format(
vname_model))
for season in seasonal_clim_fields_obs_interp:
seasonal_clim_fields_obs_interp[season] = np.ma.masked_where(
np.isnan(seasonal_clim_fields_obs_interp[season]),
seasonal_clim_fields_obs_interp[season])
season_to_err[season] = season_to_clim_fields_model_agg[
season] - seasonal_clim_fields_obs_interp[season]
if vname_model in ["I5"]:
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats,
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 --- CRU ---")
print("{}: {} \%".format(
season,
good_vals.mean() / seasonal_clim_fields_obs_interp[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=diff_axes_list)
if obs_axes_list is not None and vname_model in ["I5"]:
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
cs_obs = None
xx, yy = bmp_info_agg.get_proj_xy()
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
lons_model = None
xx_model, yy_model = None, None
cs_mod = None
norm = BoundaryNorm(clevs, 256)
for col, (season, obs_field) in enumerate(
seasonal_clim_fields_obs_interp.items()):
# Obsrved fields
ax = obs_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
to_plot = maskoceans(lons, bmp_info_agg.lats, obs_field)
cs_obs = bmp_info_agg.basemap.contourf(xx,
yy,
to_plot,
levels=clevs,
ax=ax,
norm=norm,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
ax.set_title(season)
# Model outputs
if model_axes_list is not None:
ax = model_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(
BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
if lons_model is None:
lons_model = bmp_info_model.lons.copy()
lons_model[lons_model > 180] -= 360
xx_model, yy_model = bmp_info_model.basemap(
lons_model, bmp_info_model.lats)
model_field = seasonal_clim_fields_model[season]
to_plot = maskoceans(lons_model, bmp_info_model.lats,
model_field)
cs_mod = bmp_info_agg.basemap.contourf(xx_model,
yy_model,
to_plot,
levels=cs_obs.levels,
ax=ax,
norm=cs_obs.norm,
cmap=cs_obs.cmap,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
plt.colorbar(cs_obs, cax=obs_axes_list[-1])
return cs
