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,
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 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 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_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():
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 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 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 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 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 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 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 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
