def main():
start_year = 1980
end_year = 2003
months_of_obs = [12, 1, 2, 3, 4, 5]
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
var_name = "LC"
bmp_info = analysis.get_basemap_info(r_config=r_config)
lkid_to_mask = get_lake_masks(bmp_info.lons, bmp_info.lats)
cell_area_m2 = analysis.get_array_from_file(path=r_config.data_path, var_name="cell_area_m2")
# read the model data
lkid_to_ts_model = {}
for lkid, the_mask in lkid_to_mask.items():
lkid_to_ts_model[lkid] = analysis.get_area_mean_timeseries(r_config.data_path, var_name=var_name, the_mask=the_mask * cell_area_m2,
start_year=start_year, end_year=end_year)
df = lkid_to_ts_model[lkid]
# remove the last December
df = df.select(lambda d: not (d.year == end_year and d.month == 12))
# remove the first Jan and Feb
df = df.select(lambda d: not (d.year == start_year and d.month in [1, 2]))
# remove the Feb 29th
df = df.select(lambda d: not (d.month == 2 and d.day == 29))
# select months of interest
df = df.select(lambda d: d.month in months_of_obs)
# calculate the climatology
df = df.groupby(lambda d: datetime(2001 if d.month == 12 else 2002, d.month, d.day)).mean()
df.sort_index(inplace=True)
lkid_to_ts_model[lkid] = df * 100
# read obs data and calculate climatology
lkid_to_ts_obs = {}
for lkid in LAKE_IDS:
lkid_to_ts_obs[lkid] = GL_obs_timeseries.get_ts_from_file(path=os.path.join(OBS_DATA_FOLDER, "{}-30x.TXT".format(lkid)),
start_year=start_year, end_year=end_year - 1)
# get the climatology
dfm = lkid_to_ts_obs[lkid].mean(axis=1)
dfm.index = [datetime(2001, 1, 1) + timedelta(days=int(jd - 1)) for jd in dfm.index]
lkid_to_ts_obs[lkid] = dfm
# plotting
plot_utils.apply_plot_params(font_size=10)
fig = plt.figure()
gs = GridSpec(nrows=len(lkid_to_ts_model), ncols=2)
for row, lkid in enumerate(lkid_to_ts_model):
ax = fig.add_subplot(gs[row, 0])
mod = lkid_to_ts_model[lkid]
obs = lkid_to_ts_obs[lkid]
print(obs.index)
print(obs.values)
ax.plot(mod.index, mod.values, label=r_config.label, color="r", lw=2)
ax.plot(obs.index, obs.values, label="NOAA NIC/CIS", color="k", lw=2)
if row == 0:
ax.legend()
ax.set_title(lkid)
ax.xaxis.set_major_formatter(DateFormatter("%b"))
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "GL_ice-cover-validation.png"), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def main():
vname_model = "I5"
nx_agg = 2
ny_agg = 2
start_year = 1980
end_year = 2006
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
bmp_info = analysis.get_basemap_info(r_config=r_config)
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
season_to_months = OrderedDict([
("Winter", [12, 1, 2]),
("Spring", [3, 4, 5])
])
# Get the model data
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():
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
# Get the EASE data
obs_manager = EaseSweManager()
season_to_clim_fields_obs = obs_manager.get_seasonal_clim_interpolated_to(target_lon2d=bmp_info_agg.lons, target_lat2d=bmp_info_agg.lats,
season_to_months=season_to_months, start_year=start_year, end_year=end_year)
# Do the plotting
plot_utils.apply_plot_params(font_size=10, width_cm=16, height_cm=24)
fig = plt.figure()
xx, yy = bmp_info_agg.get_proj_xy()
gs = GridSpec(3, len(season_to_clim_fields_model_agg) + 1, width_ratios=[1.0, ] * len(season_to_clim_fields_model_agg) + [0.05, ])
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
norm = BoundaryNorm(clevs, 256)
clevs_diff = np.arange(-100, 110, 10)
cs_val = None
cs_diff = None
col = 0
lons_agg_copy = bmp_info_agg.lons.copy()
lons_agg_copy[lons_agg_copy > 180] -= 360
lons_copy = bmp_info.lons.copy()
lons_copy[lons_copy > 180] -= 360
xx1, yy1 = bmp_info.get_proj_xy()
for season, mod_field in seasonal_clim_fields_model.items():
obs_field = season_to_clim_fields_obs[season]
row = 0
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
obs_field = maskoceans(lons_agg_copy, bmp_info_agg.lats, obs_field)
cs_val = bmp_info_agg.basemap.contourf(xx, yy, obs_field, levels=clevs, norm=norm, ax=ax, extend="max")
bmp_info_agg.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel("NSIDC")
row += 1
ax = fig.add_subplot(gs[row, col])
mod_field = maskoceans(lons_copy, bmp_info.lats, mod_field)
bmp_info.basemap.contourf(xx1, yy1, mod_field, levels=cs_val.levels, norm=cs_val.norm, ax=ax, extend="max")
bmp_info.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel(r_config.label)
row += 1
ax = fig.add_subplot(gs[row, col])
cs_diff = bmp_info_agg.basemap.contourf(xx, yy, season_to_clim_fields_model_agg[season] - obs_field, levels=clevs_diff, ax=ax, extend="both", cmap="seismic")
bmp_info_agg.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel("{} minus {}".format(r_config.label, "NSIDC"))
col += 1
# Add values colorbar
ax = fig.add_subplot(gs[0, -1])
plt.colorbar(cs_val, cax=ax)
ax.set_title("mm")
# Add differences colorbaar
ax = fig.add_subplot(gs[-1, -1])
plt.colorbar(cs_diff, cax=ax)
ax.set_title("mm")
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "NSIDC_vs_CRCM_swe.png"), dpi=common_plot_params.FIG_SAVE_DPI, bbox_inches="tight")
def main():
season_to_months = DEFAULT_SEASON_TO_MONTHS
varnames = ["PR", "TT"]
plot_utils.apply_plot_params(font_size=5, width_pt=None, width_cm=15, height_cm=4)
reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
xx_agg = None
yy_agg = None
for vname in varnames:
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname, season_to_months=season_to_months, bmp_info=bmp_info)
# get CRU obs values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname], vname=vname
)
if xx_agg is None:
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
# get model data
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
varname=vname,
level=0,
season_to_months=season_to_months)
###
biases_with_anusplin = OrderedDict()
biases_with_cru = OrderedDict()
nx_agg = 5
ny_agg = 5
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 == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
biases_with_cru[season] = season_to_clim_fields_model_agg[season] - season_to_obs_cru[season]
biases_with_anusplin[season] = season_to_clim_fields_model_agg[season] - aggregate_array(season_to_obs_anusplin[season], nagg_x=nx_agg, nagg_y=ny_agg)
# Do the plotting
fig = plt.figure()
clevs = [c for c in np.arange(-0.5, 0.55, 0.05)] if vname == "PR" else np.arange(-2, 2.2, 0.2)
gs = GridSpec(1, len(biases_with_cru) + 1, width_ratios=len(biases_with_cru) * [1., ] + [0.05, ])
col = 0
cs = None
cmap = "seismic"
fig.suptitle(r"$\left| \delta_{\rm Hopkinson} \right| - \left| \delta_{\rm CRU} \right|$")
for season, cru_err in biases_with_cru.items():
anu_err = biases_with_anusplin[season]
ax = fig.add_subplot(gs[0, col])
diff = np.abs(anu_err) - np.abs(cru_err)
cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, diff, levels=clevs, ax=ax, extend="both", cmap=cmap)
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
good = diff[~diff.mask & ~np.isnan(diff)]
n_neg = sum(good < 0) / sum(good > 0)
print("season: {}, n-/n+ = {}".format(season, n_neg))
ax.set_title(season)
ax.set_xlabel(r"$n_{-}/n_{+} = $" + "{:.1f}".format(n_neg) + "\n" + r"$\overline{\varepsilon} = $" + "{:.2f}".format(good.mean()))
col += 1
ax = fig.add_subplot(gs[0, -1])
plt.colorbar(cs, cax=ax)
ax.set_title("mm/day" if vname == "PR" else r"${\rm ^\circ C}$")
fig.savefig(os.path.join(img_folder, "comp_anu_and_cru_biases_for_{}.png".format(vname)), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def main():
lkfr_limit = 0.05
model_data_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
start_year=start_year_c, end_year=end_year_c
)
params.update(
dict(data_path=model_data_current_path, label=modif_label)
)
model_config_c = RunConfig(**params)
model_config_f = model_config_c.get_shifted_config(future_shift_years)
bmp_info = analysis.get_basemap_info(r_config=model_config_c)
specific_cond_heat = 0.250100e7 # J/kg
water_density = 1000.0 # kg/m**3
season_to_months = OrderedDict([
("Summer", [6, 7, 8]),
])
lkfr = analysis.get_array_from_file(path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", var_name=infovar.HDF_LAKE_FRACTION_NAME)
assert lkfr is not None, "Could not find lake fraction in the file"
# Current climate
traf_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TRAF", level=5, season_to_months=season_to_months)
pr_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="PR", level=0, season_to_months=season_to_months)
lktemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="L1", level=0, season_to_months=season_to_months)
airtemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TT", level=0, season_to_months=season_to_months)
lhc = OrderedDict([
(s, specific_cond_heat * (pr_c[s] * water_density - traf_c[s])) for s, traf in traf_c.items()
])
avc = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="AV", level=0, season_to_months=season_to_months)
plt.figure()
lhc["Summer"] = np.ma.masked_where(lkfr < lkfr_limit, lhc["Summer"])
print("min: {}, max: {}".format(lhc["Summer"].min(), lhc["Summer"].max()))
cs = plt.contourf(lhc["Summer"].T)
plt.title("lhc")
plt.colorbar()
plt.figure()
cs = plt.contourf(avc["Summer"].T, levels=cs.levels, norm=cs.norm, cmap=cs.cmap)
plt.title("avc")
plt.colorbar()
# Future climate
traf_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TRAF", level=5, season_to_months=season_to_months)
pr_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="PR", level=0,
season_to_months=season_to_months)
lktemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="L1", level=0,
season_to_months=season_to_months)
airtemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TT", level=0,
season_to_months=season_to_months)
lhf = OrderedDict([
(s, specific_cond_heat * (pr_f[s] * water_density - traf_f[s])) for s, traf in traf_f.items()
])
plt.figure()
plt.pcolormesh(traf_c["Summer"].T)
plt.title("TRAF over lakes current")
plt.colorbar()
avf = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="AV", level=0,
season_to_months=season_to_months)
plt.figure()
cs = plt.contourf(avf["Summer"].T)
plt.title("avf")
plt.colorbar()
plt.figure()
cs = plt.contourf(avf["Summer"].T - avc["Summer"].T, levels=np.arange(-40, 45, 5))
plt.title("d(av)")
plt.colorbar()
plt.figure()
plt.contourf(lhf["Summer"].T - lhc["Summer"].T, levels=cs.levels, cmap=cs.cmap, norm=cs.norm)
plt.title("d(lh)")
plt.colorbar()
# plotting
plot_utils.apply_plot_params(width_cm=15, height_cm=15, font_size=10)
gs = GridSpec(2, 2)
# tair_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tair_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
#
#
# tlake_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tlake_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
for season in season_to_months:
fig = plt.figure()
lktemp_c[season] -= 273.15
dT_c = np.ma.masked_where(lkfr < lkfr_limit, lktemp_c[season] - airtemp_c[season])
lktemp_f[season] -= 273.15
dT_f = np.ma.masked_where(lkfr < lkfr_limit, lktemp_f[season] - airtemp_f[season])
d = np.round(max(np.ma.abs(dT_c).max(), np.ma.abs(dT_f).max()))
vmin = -d
vmax = d
clevs = np.arange(-12, 13, 1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
ax_list = []
fig.suptitle(season)
xx, yy = bmp_info.get_proj_xy()
# Current gradient
ax = fig.add_subplot(gs[0, 0])
ax.set_title(r"current: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_c, ax=ax, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Future Gradient
ax = fig.add_subplot(gs[0, 1])
ax.set_title(r"future: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_f, ax=ax, norm=cs.norm, cmap=cs.cmap, vmin=vmin, vmax=vmax)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the gradient
ax = fig.add_subplot(gs[1, 0])
ax.set_title(r"$\Delta T_{\rm future} - \Delta T_{\rm current}$")
ddT = dT_f - dT_c
d = np.round(np.ma.abs(ddT).max())
clevs = np.arange(-3, 3.1, 0.1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
cs = bmp_info.basemap.pcolormesh(xx, yy, ddT, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the latent heat flux
# ax = fig.add_subplot(gs[1, 1])
# ax.set_title(r"$LE_{\rm future} - LE_{\rm current}$")
# dlh = np.ma.masked_where(lkfr < lkfr_limit, lhf[season] - lhc[season])
#
# d = np.round(np.ma.abs(dlh).max() // 10) * 10
# clevs = np.arange(0, 105, 5)
# bn = BoundaryNorm(clevs, ncolors=ncolors)
# cmap = cm.get_cmap("jet", ncolors)
#
# cs = bmp_info.basemap.pcolormesh(xx, yy, dlh, norm=bn, cmap=cmap)
# bmp_info.basemap.colorbar(cs, ax=ax, extend="max") # Change in the latent heat flux
# ax_list.append(ax)
for the_ax in ax_list:
bmp_info.basemap.drawcoastlines(linewidth=0.3, ax=the_ax)
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "lake_atm_gradients_and_fluxes_{}-{}_{}-{}.png".format(model_config_f.start_year, model_config_f.end_year, start_year_c, end_year_c)),
dpi=800,
bbox_inches="tight")
def main():
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
season_to_months = OrderedDict([
("Winter (DJF)", (1, 2, 12)),
("Spring (MAM)", range(3, 6)),
("Summer (JJA)", range(6, 9)),
("Fall (SON)", range(9, 12)),
])
varnames = ["TT", "PR"]
plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20, height_cm=17)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
#
reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980, end_year=2010, label="ERAI-CRCM5-L(0.4)")
nx_agg_model = 1
ny_agg_model = 1
nx_agg_anusplin = 4
ny_agg_anusplin = 4
gcm_driven_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980, end_year=2010, label="CanESM2-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
xx, yy = bmp_info.get_proj_xy()
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
for vname in varnames:
fig = plt.figure()
ncols = len(season_to_months)
gs = GridSpec(4, ncols + 1, width_ratios=ncols * [1., ] + [0.09, ])
clevels = vname_to_clevels[vname]
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname, season_to_months=season_to_months, bmp_info=bmp_info,
n_agg_x=nx_agg_anusplin, n_agg_y=ny_agg_anusplin)
row = 0
# Plot CRU values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname], vname=vname
)
# Mask out the Great Lakes
cru_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"))
for season in season_to_obs_cru:
season_to_obs_cru[season] = np.ma.masked_where(cru_mask > 0.5, season_to_obs_cru[season])
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
for j, (season, obs_field) in enumerate(season_to_obs_cru.items()):
ax = ax_list[j]
cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, obs_field.copy(), levels=clevels, ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("CRU")
# plt.colorbar(cs, caax=ax_list[-1])
row += 1
# Plot ANUSPLIN values-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
for j, (season, obs_field) in enumerate(season_to_obs_anusplin.items()):
ax = ax_list[j]
cs = bmp_info.basemap.contourf(xx, yy, obs_field, levels=clevels, ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("Hopkinson")
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
_format_axes(ax_list, vname=vname)
row += 1
# Plot model (CRCM) values-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# cs = None
#
# season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
# varname=vname, level=0,
# season_to_months=season_to_months)
#
# for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()):
# ax = ax_list[j]
# cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax)
# bmp_info.basemap.drawcoastlines(ax=ax)
# bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
# ax.set_title(season)
#
# ax_list[0].set_ylabel(reanalysis_driven_config.label)
# cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
# cb.ax.set_xlabel(infovar.get_units(vname))
# _format_axes(ax_list, vname=vname)
# row += 1
# Plot (Model - CRU) Performance biases-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = plot_performance_err_with_cru.compare_vars(vname_model=vname, vname_obs=None,
r_config=reanalysis_driven_config,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
bmp_info_agg=bmp_info_agg, diff_axes_list=ax_list,
mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"),
nx_agg_model=nx_agg_model, ny_agg_model=ny_agg_model)
ax_list[0].set_ylabel("{label}\n--\nCRU".format(label=reanalysis_driven_config.label))
_format_axes(ax_list, vname=vname)
row += 1
# Plot performance+BFE errors with respect to CRU (Model - CRU)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname],
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info_agg, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label))
# row += 1
# Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
r_config=reanalysis_driven_config,
bmp_info_agg=bmp_info, season_to_months=season_to_months,
axes_list=ax_list)
_format_axes(ax_list, vname=vname)
ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(label=reanalysis_driven_config.label))
row += 1
# Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label))
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
# Save the plot
img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format(
vname=vname, sy=reanalysis_driven_config.start_year, ey=reanalysis_driven_config.end_year,
sim_label=reanalysis_driven_config.label)
img_file = img_folder.joinpath(img_file)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main():
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
season_to_months = OrderedDict([
("Winter (DJF)", (1, 2, 12)),
("Spring (MAM)", range(3, 6)),
("Summer (JJA)", range(6, 9)),
("Fall (SON)", range(9, 12)),
])
varnames = ["TT", "PR"]
plot_utils.apply_plot_params(font_size=10,
width_pt=None,
width_cm=20,
height_cm=17)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
#
reanalysis_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980,
end_year=2010,
label="ERAI-CRCM5-L(0.4)")
nx_agg_model = 1
ny_agg_model = 1
nx_agg_anusplin = 4
ny_agg_anusplin = 4
gcm_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980,
end_year=2010,
label="CanESM2-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
xx, yy = bmp_info.get_proj_xy()
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2),
"PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
for vname in varnames:
fig = plt.figure()
ncols = len(season_to_months)
gs = GridSpec(4, ncols + 1, width_ratios=ncols * [
1.,
] + [
0.09,
])
clevels = vname_to_clevels[vname]
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname,
season_to_months=season_to_months,
bmp_info=bmp_info,
n_agg_x=nx_agg_anusplin,
n_agg_y=ny_agg_anusplin)
row = 0
# Plot CRU values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
bmp_info=bmp_info,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
vname=vname)
# Mask out the Great Lakes
cru_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"))
for season in season_to_obs_cru:
season_to_obs_cru[season] = np.ma.masked_where(
cru_mask > 0.5, season_to_obs_cru[season])
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
for j, (season, obs_field) in enumerate(season_to_obs_cru.items()):
ax = ax_list[j]
cs = bmp_info_agg.basemap.contourf(xx_agg,
yy_agg,
obs_field.copy(),
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("CRU")
# plt.colorbar(cs, caax=ax_list[-1])
row += 1
# Plot ANUSPLIN values-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
for j, (season,
obs_field) in enumerate(season_to_obs_anusplin.items()):
ax = ax_list[j]
cs = bmp_info.basemap.contourf(xx,
yy,
obs_field,
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("Hopkinson")
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
_format_axes(ax_list, vname=vname)
row += 1
# Plot model (CRCM) values-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# cs = None
#
# season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
# varname=vname, level=0,
# season_to_months=season_to_months)
#
# for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()):
# ax = ax_list[j]
# cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax)
# bmp_info.basemap.drawcoastlines(ax=ax)
# bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
# ax.set_title(season)
#
# ax_list[0].set_ylabel(reanalysis_driven_config.label)
# cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
# cb.ax.set_xlabel(infovar.get_units(vname))
# _format_axes(ax_list, vname=vname)
# row += 1
# Plot (Model - CRU) Performance biases-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = plot_performance_err_with_cru.compare_vars(
vname_model=vname,
vname_obs=None,
r_config=reanalysis_driven_config,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
bmp_info_agg=bmp_info_agg,
diff_axes_list=ax_list,
mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"),
nx_agg_model=nx_agg_model,
ny_agg_model=ny_agg_model)
ax_list[0].set_ylabel(
"{label}\n--\nCRU".format(label=reanalysis_driven_config.label))
_format_axes(ax_list, vname=vname)
row += 1
# Plot performance+BFE errors with respect to CRU (Model - CRU)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname],
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info_agg, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label))
# row += 1
# Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
plot_performance_err_with_anusplin.compare_vars(
vname, {vname: season_to_obs_anusplin},
r_config=reanalysis_driven_config,
bmp_info_agg=bmp_info,
season_to_months=season_to_months,
axes_list=ax_list)
_format_axes(ax_list, vname=vname)
ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(
label=reanalysis_driven_config.label))
row += 1
# Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label))
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
# Save the plot
img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format(
vname=vname,
sy=reanalysis_driven_config.start_year,
ey=reanalysis_driven_config.end_year,
sim_label=reanalysis_driven_config.label)
img_file = img_folder.joinpath(img_file)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)