def validate_seas_mean_lswt_from_hostetler_and_nemo_with_homa(
hl_data_path="/home/huziy/skynet3_rech1/CRCM_GL_simulation/all_files",
start_year=2003, end_year=2006):
crcm5_model_manager = Crcm5ModelDataManager(samples_folder_path=hl_data_path, all_files_in_samples_folder=True)
varname = "L1"
season = "Fall"
season_to_months = {season: range(9, 11)}
season_months = list(season_to_months[season])
print(season_months, season_to_months)
hl_lake_temp_clim = crcm5_model_manager.get_mean_field(start_year=start_year, end_year=end_year, var_name=varname,
level=1.1, months=season_months)
hl_lake_temp_clim = hl_lake_temp_clim[13:-13, 13:-13]
print("hl_lake_temp_clim.shape = ", hl_lake_temp_clim.shape)
lon_hl, lat_hl = crcm5_model_manager.lons2D, crcm5_model_manager.lats2D
print("lon_hl.shape = ", lon_hl.shape)
print(lon_hl.min(), lon_hl.max())
print(lat_hl.min(), lat_hl.max())
# Get Nemo manager here only for coordinates and mask
nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
suffix="icemod.nc")
lon2d, lat2d, bmp = nemo_manager.get_coords_and_basemap()
# Interpolate hostetler's lake fraction to the model's grid
hl_lake_temp_clim -= 273.15
xs, ys, zs = lat_lon.lon_lat_to_cartesian(lon_hl.flatten(), lat_hl.flatten())
print(xs.shape)
ktree = cKDTree(data=list(zip(xs, ys, zs)))
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lon2d.flatten(), lat2d.flatten())
dists, inds = ktree.query(np.asarray(list(zip(xt, yt, zt))))
print(inds[:20])
print(type(inds))
print(inds.min(), inds.max())
hl_lake_temp_clim = hl_lake_temp_clim.flatten()[inds].reshape(lon2d.shape)
# get nemo and observed sst
obs_sst, nemo_sst, _, _, _ = nemo_manager.get_nemo_and_homa_seasonal_mean_sst(start_year=start_year,
end_year=end_year,
season_to_months=season_to_months)
obs_sst_clim = np.ma.mean([obs_sst[y][season] for y in range(start_year, end_year + 1)], axis=0)
nemo_sst_clim = np.ma.mean([nemo_sst[y][season] for y in range(start_year, end_year + 1)], axis=0)
obs_sst_clim = np.ma.masked_where(~nemo_manager.lake_mask, obs_sst_clim)
nemo_sst_clim = np.ma.masked_where(~nemo_manager.lake_mask, nemo_sst_clim)
hl_lake_temp_clim = np.ma.masked_where(~nemo_manager.lake_mask, hl_lake_temp_clim)
# plt.figure()
xx, yy = bmp(lon2d.copy(), lat2d.copy())
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
vmin = min(obs_sst_clim.min(), nemo_sst_clim.min(), hl_lake_temp_clim.min())
vmax = max(obs_sst_clim.max(), nemo_sst_clim.max(), hl_lake_temp_clim.max())
print("vmin={}; vmax={}".format(vmin, vmax))
# plt.figure()
# b = Basemap()
# xx, yy = b(lons_obs, lats_obs)
# im = b.pcolormesh(xx, yy, obs_yearmax_ice_conc)
# b.colorbar(im)
# b.drawcoastlines()
# Plot as usual: model, obs, model - obs
img_folder = Path("nemo/hostetler")
if not img_folder.is_dir():
img_folder.mkdir()
img_file = img_folder.joinpath("validate_{}_lswt_hostetler_nemo_vs_homa_{}-{}.png".format(
season, start_year, end_year))
fig = plt.figure()
gs = GridSpec(1, 4, width_ratios=[1, 1, 1, 0.05])
all_axes = []
cmap = cm.get_cmap("jet", 10)
locator = MaxNLocator(nbins=10)
ticks = locator.tick_values(vmin=vmin, vmax=vmax)
norm = BoundaryNorm(boundaries=ticks, ncolors=len(ticks) - 1)
# Model, Hostetler
ax = fig.add_subplot(gs[0, 0])
ax.set_title("Hostetler+CRCM5")
bmp.pcolormesh(xx, yy, hl_lake_temp_clim, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
all_axes.append(ax)
#
ax = fig.add_subplot(gs[0, 1])
ax.set_title("NEMO-offline")
im = bmp.pcolormesh(xx, yy, nemo_sst_clim, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
all_axes.append(ax)
# Obs: Homa
ax = fig.add_subplot(gs[0, 2])
ax.set_title("MODIS")
im = bmp.pcolormesh(xx, yy, obs_sst_clim, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
all_axes.append(ax)
plt.colorbar(im, cax=fig.add_subplot(gs[0, -1]), ticks=ticks)
for the_ax in all_axes:
bmp.drawcoastlines(ax=the_ax)
fig.savefig(str(img_file), bbox_inches="tight")
plt.close(fig)
def validate_seas_mean_lswt_from_hostetler_and_nemo_with_homa(
hl_data_path="/home/huziy/skynet3_rech1/CRCM_GL_simulation/all_files",
start_year=2003,
end_year=2003,
season_to_months=None):
"""
Note: degrees plotted are in C
:param hl_data_path:
:param start_year:
:param end_year:
"""
# crcm5_model_manager = Crcm5ModelDataManager(samples_folder_path=hl_data_path, all_files_in_samples_folder=True)
model_label1 = "CRCM5_HL"
plot_hl_biases = True
use_noaa_oisst = True
obs_label = "NOAA OISST" if use_noaa_oisst else "MODIS"
clevs = np.arange(0, 22, 1)
norm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("viridis", len(clevs) - 1)
clevs_bias = np.arange(-5.5, 6.5, 1)
norm_bias = BoundaryNorm(clevs_bias, len(clevs_bias) - 1)
cmap_bias = cm.get_cmap("bwr", len(clevs_bias) - 1)
if season_to_months is None:
season_to_months = OrderedDict([
("Winter", [1, 2, 12]),
("Spring", [3, 4, 5]),
("Summer", range(6, 9)),
("Fall", range(9, 11)),
])
# hl_lake_temp_clim = crcm5_model_manager.get_mean_field(start_year=start_year, end_year=end_year, var_name=varname,
# level=1.1, months=season_months)
# Get Nemo manager here only for coordinates and mask
# nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
# suffix="icemod.nc")
# nemo_manager = NemoYearlyFilesManager(folder="/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_5min",
# suffix="grid_T.nc")
model_label2 = "CRCM5_NEMO"
nemo_manager = NemoYearlyFilesManager(
folder="/BIG1/huziy/CRCM5_NEMO_coupled_sim_nemo_outputs/NEMO",
suffix="grid_T.nc")
# model_label2 = "NEMO_offline_CRCM5_CanESM2"
# nemo_manager = NemoYearlyFilesManager(folder="/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3_CC_drivenby_CRCM5_CanESM2_RCP85/EXP00/cc_canesm2_outputs",
# suffix="grid_T.nc")
# model_label2 = "NEMO_offline"
# nemo_manager = NemoYearlyFilesManager(folder="/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_30min",
# suffix="grid_T.nc")
img_folder = Path("nemo/{}".format(model_label2))
#lon2d, lat2d, bmp = nemo_manager.get_coords_and_basemap(resolution="l", subregion=[0.06, 0.5, 0.06, 0.5])
lon2d, lat2d, bmp = nemo_manager.get_coords_and_basemap(
resolution="l", subregion=[0.1, 0.5, 0.15, 0.5], area_thresh=2000)
xx, yy = bmp(lon2d, lat2d)
# get nemo and observed sst
nemo_sst, obs_sst, _, _, _ = nemo_manager.get_nemo_and_homa_seasonal_mean_sst(
start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
use_noaa_oisst=use_noaa_oisst)
obs_sst_clim = {}
if use_noaa_oisst:
manager = OISSTManager(thredds_baseurl="/BIG1/huziy/noaa_oisst_daily")
obs_sst_clim = manager.get_seasonal_clim_interpolate_to(
lons=lon2d,
lats=lat2d,
start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
vname="sst")
for season in season_to_months:
obs_sst_clim[season] = np.ma.masked_where(
np.isnan(obs_sst_clim[season]), obs_sst_clim[season])
else:
# Convert to Celsius
for season in season_to_months:
obs_sst_clim[season] = np.ma.mean(
[obs_sst[y][season] for y in range(start_year, end_year + 1)],
axis=0) - 273.15
obs_sst_clim = {
season: np.ma.masked_where(~nemo_manager.lake_mask,
obs_sst_clim[season])
for season in obs_sst_clim
}
nemo_sst_clim = {
season: np.ma.mean(
[nemo_sst[y][season] for y in range(start_year, end_year + 1)],
axis=0)
for season in season_to_months
}
nemo_sst_clim = {
season: np.ma.masked_where(~nemo_manager.lake_mask,
nemo_sst_clim[season])
for season in season_to_months
}
hl_sst_clim = {}
if plot_hl_biases:
hl_sst_clim = get_seasonal_sst_from_crcm5_outputs(
model_label1,
start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
lons_target=lon2d,
lats_target=lat2d)
hl_sst_clim = {
season: np.ma.masked_where(~nemo_manager.lake_mask,
hl_sst_clim[season])
for season in season_to_months
}
# Convert to C
hl_sst_clim = {
season: hl_sst_clim[season] - 273.15
for season in season_to_months
}
# plt.figure()
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
# plt.figure()
# b = Basemap()
# xx, yy = b(lons_obs, lats_obs)
# im = b.pcolormesh(xx, yy, obs_yearmax_ice_conc)
# b.colorbar(im)
# b.drawcoastlines()
# Plot as usual: model, obs, model - obs
if not img_folder.is_dir():
img_folder.mkdir()
img_file = img_folder.joinpath(
"validate_{}_lswt_{}_vs_{}_{}-{}.png".format(
"_".join([season for season in season_to_months]),
"_".join([model_label1 if plot_hl_biases else "",
model_label2]), obs_label, start_year, end_year))
nrows = 3
plot_utils.apply_plot_params(font_size=8,
width_cm=8 * len(season_to_months),
height_cm=4.5 * nrows)
fig = plt.figure()
gs = GridSpec(nrows=nrows,
ncols=len(season_to_months),
hspace=0.15,
wspace=0.000)
all_axes = []
# Model, Hostetler
# ax = fig.add_subplot(gs[0, 0])
# ax.set_title("Hostetler+CRCM5")
# bmp.pcolormesh(xx, yy, hl_lake_temp_clim, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
# col += 1
# all_axes.append(ax)
for col, season in enumerate(season_to_months):
row = 0
# Obs: MODIS or NOAA OISST
ax = fig.add_subplot(gs[row, col])
im_obs = bmp.pcolormesh(xx,
yy,
obs_sst_clim[season],
cmap=cmap,
norm=norm)
# obs values
cb = bmp.colorbar(im_obs, location="bottom")
cb.ax.set_visible(col == 0)
ax.text(0.99,
0.99,
season,
va="top",
ha="right",
fontsize=16,
transform=ax.transAxes)
all_axes.append(ax)
if col == 0:
ax.set_ylabel(obs_label)
row += 1
if plot_hl_biases:
#plot CRCM5_HL biases (for poster)
ax = fig.add_subplot(gs[row, col])
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(model_label1, obs_label))
im_bias = bmp.pcolormesh(xx,
yy,
hl_sst_clim[season] -
obs_sst_clim[season],
cmap=cmap_bias,
norm=norm_bias,
ax=ax)
cb = bmp.colorbar(im_bias, location="bottom", ax=ax)
cb.ax.set_visible(False)
all_axes.append(ax)
row += 1
ax = fig.add_subplot(gs[row, col])
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(model_label2, obs_label))
im_bias = bmp.pcolormesh(xx,
yy,
nemo_sst_clim[season] - obs_sst_clim[season],
cmap=cmap_bias,
norm=norm_bias)
# common for all bias values
cb = bmp.colorbar(im_bias, location="bottom", ax=ax)
cb.ax.set_visible(col == 0)
all_axes.append(ax)
row += 1
print_arr_limits(nemo_sst_clim[season],
"NEMO_sst, for {}".format(season))
print_arr_limits(obs_sst_clim[season],
"Obs_sst, for {}".format(season))
for the_ax in all_axes:
bmp.drawcoastlines(ax=the_ax, linewidth=0.3)
the_ax.set_frame_on(False)
print("Saving {}".format(img_file))
fig.savefig(str(img_file), bbox_inches="tight", dpi=300)
plt.close(fig)
def validate_seas_mean_lswt_from_hostetler_and_nemo_with_homa(
hl_data_path="/home/huziy/skynet3_rech1/CRCM_GL_simulation/all_files",
start_year=2003, end_year=2003, season_to_months=None):
"""
Note: degrees plotted are in C
:param hl_data_path:
:param start_year:
:param end_year:
"""
# crcm5_model_manager = Crcm5ModelDataManager(samples_folder_path=hl_data_path, all_files_in_samples_folder=True)
model_label1 = "CRCM5_HL"
plot_hl_biases = True
use_noaa_oisst = True
obs_label = "NOAA OISST" if use_noaa_oisst else "MODIS"
clevs = np.arange(0, 22, 1)
norm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("viridis", len(clevs) - 1)
clevs_bias = np.arange(-5.5, 6.5, 1)
norm_bias = BoundaryNorm(clevs_bias, len(clevs_bias) - 1)
cmap_bias = cm.get_cmap("bwr", len(clevs_bias) - 1)
if season_to_months is None:
season_to_months = OrderedDict([
("Winter", [1, 2, 12]),
("Spring", [3, 4, 5]),
("Summer", range(6, 9)),
("Fall", range(9, 11)),
])
# hl_lake_temp_clim = crcm5_model_manager.get_mean_field(start_year=start_year, end_year=end_year, var_name=varname,
# level=1.1, months=season_months)
# Get Nemo manager here only for coordinates and mask
# nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
# suffix="icemod.nc")
# nemo_manager = NemoYearlyFilesManager(folder="/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_5min",
# suffix="grid_T.nc")
model_label2 = "CRCM5_NEMO"
nemo_manager = NemoYearlyFilesManager(folder="/BIG1/huziy/CRCM5_NEMO_coupled_sim_nemo_outputs/NEMO",
suffix="grid_T.nc")
# model_label2 = "NEMO_offline_CRCM5_CanESM2"
# nemo_manager = NemoYearlyFilesManager(folder="/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3_CC_drivenby_CRCM5_CanESM2_RCP85/EXP00/cc_canesm2_outputs",
# suffix="grid_T.nc")
# model_label2 = "NEMO_offline"
# nemo_manager = NemoYearlyFilesManager(folder="/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_30min",
# suffix="grid_T.nc")
img_folder = Path("nemo/{}".format(model_label2))
#lon2d, lat2d, bmp = nemo_manager.get_coords_and_basemap(resolution="l", subregion=[0.06, 0.5, 0.06, 0.5])
lon2d, lat2d, bmp = nemo_manager.get_coords_and_basemap(resolution="l", subregion=[0.1, 0.5, 0.15, 0.5], area_thresh=2000)
xx, yy = bmp(lon2d, lat2d)
# get nemo and observed sst
nemo_sst, obs_sst, _, _, _ = nemo_manager.get_nemo_and_homa_seasonal_mean_sst(start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
use_noaa_oisst=use_noaa_oisst)
obs_sst_clim = {}
if use_noaa_oisst:
manager = OISSTManager(thredds_baseurl="/BIG1/huziy/noaa_oisst_daily")
obs_sst_clim = manager.get_seasonal_clim_interpolate_to(lons=lon2d, lats=lat2d,
start_year=start_year, end_year=end_year,
season_to_months=season_to_months, vname="sst")
for season in season_to_months:
obs_sst_clim[season] = np.ma.masked_where(np.isnan(obs_sst_clim[season]), obs_sst_clim[season])
else:
# Convert to Celsius
for season in season_to_months:
obs_sst_clim[season] = np.ma.mean([obs_sst[y][season] for y in range(start_year, end_year + 1)], axis=0) - 273.15
obs_sst_clim = {season: np.ma.masked_where(~nemo_manager.lake_mask, obs_sst_clim[season]) for season in obs_sst_clim}
nemo_sst_clim = {season: np.ma.mean([nemo_sst[y][season] for y in range(start_year, end_year + 1)], axis=0) for season in season_to_months}
nemo_sst_clim = {season: np.ma.masked_where(~nemo_manager.lake_mask, nemo_sst_clim[season]) for season in season_to_months}
hl_sst_clim = {}
if plot_hl_biases:
hl_sst_clim = get_seasonal_sst_from_crcm5_outputs(model_label1, start_year=start_year, end_year=end_year,
season_to_months=season_to_months,
lons_target=lon2d, lats_target=lat2d)
hl_sst_clim = {season: np.ma.masked_where(~nemo_manager.lake_mask, hl_sst_clim[season]) for season in season_to_months}
# Convert to C
hl_sst_clim = {season: hl_sst_clim[season] - 273.15 for season in season_to_months}
# plt.figure()
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
# plt.figure()
# b = Basemap()
# xx, yy = b(lons_obs, lats_obs)
# im = b.pcolormesh(xx, yy, obs_yearmax_ice_conc)
# b.colorbar(im)
# b.drawcoastlines()
# Plot as usual: model, obs, model - obs
if not img_folder.is_dir():
img_folder.mkdir()
img_file = img_folder.joinpath("validate_{}_lswt_{}_vs_{}_{}-{}.png".format(
"_".join([season for season in season_to_months]),
"_".join([model_label1 if plot_hl_biases else "", model_label2]),
obs_label,
start_year, end_year))
nrows = 3
plot_utils.apply_plot_params(font_size=8, width_cm=8 * len(season_to_months), height_cm=4.5 * nrows)
fig = plt.figure()
gs = GridSpec(nrows=nrows, ncols=len(season_to_months), hspace=0.15, wspace=0.000)
all_axes = []
# Model, Hostetler
# ax = fig.add_subplot(gs[0, 0])
# ax.set_title("Hostetler+CRCM5")
# bmp.pcolormesh(xx, yy, hl_lake_temp_clim, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
# col += 1
# all_axes.append(ax)
for col, season in enumerate(season_to_months):
row = 0
# Obs: MODIS or NOAA OISST
ax = fig.add_subplot(gs[row, col])
im_obs = bmp.pcolormesh(xx, yy, obs_sst_clim[season], cmap=cmap, norm=norm)
# obs values
cb = bmp.colorbar(im_obs, location="bottom")
cb.ax.set_visible(col == 0)
ax.text(0.99, 0.99, season, va="top", ha="right", fontsize=16, transform=ax.transAxes)
all_axes.append(ax)
if col == 0:
ax.set_ylabel(obs_label)
row += 1
if plot_hl_biases:
#plot CRCM5_HL biases (for poster)
ax = fig.add_subplot(gs[row, col])
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(model_label1, obs_label))
im_bias = bmp.pcolormesh(xx, yy, hl_sst_clim[season] - obs_sst_clim[season], cmap=cmap_bias, norm=norm_bias, ax=ax)
cb = bmp.colorbar(im_bias, location="bottom", ax=ax)
cb.ax.set_visible(False)
all_axes.append(ax)
row += 1
ax = fig.add_subplot(gs[row, col])
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(model_label2, obs_label))
im_bias = bmp.pcolormesh(xx, yy, nemo_sst_clim[season] - obs_sst_clim[season], cmap=cmap_bias, norm=norm_bias)
# common for all bias values
cb = bmp.colorbar(im_bias, location="bottom", ax=ax)
cb.ax.set_visible(col == 0)
all_axes.append(ax)
row += 1
print_arr_limits(nemo_sst_clim[season], "NEMO_sst, for {}".format(season))
print_arr_limits(obs_sst_clim[season], "Obs_sst, for {}".format(season))
for the_ax in all_axes:
bmp.drawcoastlines(ax=the_ax, linewidth=0.3)
the_ax.set_frame_on(False)
print("Saving {}".format(img_file))
fig.savefig(str(img_file), bbox_inches="tight", dpi=300)
plt.close(fig)
