def validate_yearmax_ice_cover_from_hostetler_with_glerl(path="/home/huziy/skynet3_rech1/CRCM_GL_simulation/all_files",
start_year=2003, end_year=2009):
model_manager = Crcm5ModelDataManager(samples_folder_path=path, all_files_in_samples_folder=True)
varname = "LC"
hl_icecov_yearly_max = model_manager.get_yearmax_fields(start_year=start_year, end_year=end_year, var_name=varname)
hl_icecov_yearly_max_clim = np.mean(list(hl_icecov_yearly_max.values()), axis=0)
# 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_icecov_yearly_max_clim = model_manager.interpolate_data_to(hl_icecov_yearly_max_clim, lon2d, lat2d)
hl_icecov_yearly_max_clim = np.ma.masked_where(~nemo_manager.lake_mask, hl_icecov_yearly_max_clim)
model_lake_avg_ts = []
for the_year in range(start_year, end_year + 1):
model_lake_avg_ts.append(hl_icecov_yearly_max[the_year][nemo_manager.lake_mask].mean())
# plt.figure()
xx, yy = bmp(lon2d.copy(), lat2d.copy())
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
# Read and interpolate obs
path_to_obs = "/RESCUE/skynet3_rech1/huziy/nemo_obs_for_validation/glerl_icecov.nc"
obs_varname = "ice_cover"
obs_lake_avg_ts = []
with Dataset(path_to_obs) as ds:
time_var = ds.variables["time"]
lons_obs = ds.variables["lon"][:]
lats_obs = ds.variables["lat"][:]
dates = num2date(time_var[:], time_var.units)
nx, ny = lons_obs.shape
data = ds.variables[obs_varname][:]
data = np.ma.masked_where((data > 100) | (data < 0), data)
print(data.min(), data.max())
panel = pd.Panel(data=data, items=dates, major_axis=range(nx), minor_axis=range(ny))
panel = panel.select(lambda d: start_year <= d.year <= end_year)
the_max_list = []
for key, g in panel.groupby(lambda d: d.year, axis="items"):
the_max_field = np.ma.max(np.ma.masked_where((g.values > 100) | (g.values < 0), g.values), axis=0)
obs_lake_avg_ts.append(the_max_field.mean())
the_max_list.append(the_max_field)
obs_yearmax_ice_conc = np.ma.mean(the_max_list, axis=0) / 100.0
xs, ys, zs = lat_lon.lon_lat_to_cartesian(lons_obs.flatten(), lats_obs.flatten())
ktree = cKDTree(list(zip(xs, ys, zs)))
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lon2d.flatten(), lat2d.flatten())
dists, inds = ktree.query(list(zip(xt, yt, zt)))
obs_yearmax_ice_conc_interp = obs_yearmax_ice_conc.flatten()[inds].reshape(lon2d.shape)
obs_yearmax_ice_conc_interp = np.ma.masked_where(~nemo_manager.lake_mask, obs_yearmax_ice_conc_interp)
# 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_yearmax_icecov_hl_vs_glerl_{}-{}.pdf".format(start_year, end_year))
fig = plt.figure()
gs = GridSpec(2, 4, width_ratios=[1, 1, 1, 0.05])
all_axes = []
cmap = cm.get_cmap("jet", 10)
diff_cmap = cm.get_cmap("RdBu_r", 10)
# Model
ax = fig.add_subplot(gs[0, 0])
ax.set_title("Hostetler")
bmp.pcolormesh(xx, yy, hl_icecov_yearly_max_clim, cmap=cmap, vmin=0, vmax=1)
all_axes.append(ax)
# Obs
ax = fig.add_subplot(gs[0, 2])
ax.set_title("GLERL")
im = bmp.pcolormesh(xx, yy, obs_yearmax_ice_conc_interp, cmap=cmap, vmin=0, vmax=1)
all_axes.append(ax)
plt.colorbar(im, cax=fig.add_subplot(gs[0, -1]))
# Biases
ax = fig.add_subplot(gs[1, :])
ax.set_title("Hostetler - GLERL")
im = bmp.pcolormesh(xx, yy, hl_icecov_yearly_max_clim - obs_yearmax_ice_conc_interp,
cmap=diff_cmap, vmin=-1, vmax=1)
bmp.colorbar(im, ax=ax)
all_axes.append(ax)
for the_ax in all_axes:
bmp.drawcoastlines(ax=the_ax)
fig.savefig(str(img_file), bbox_inches="tight")
plt.close(fig)
# Plot lake aversged ice concentrations
fig = plt.figure()
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.plot(range(start_year, end_year + 1), model_lake_avg_ts, "b", lw=2, label="Hostetler")
plt.plot(range(start_year, end_year + 1), np.asarray(obs_lake_avg_ts) / 100.0, "r", lw=2, label="GLERL")
plt.grid()
plt.legend(loc=3)
fig.savefig(str(img_folder.joinpath("lake_avg_iceconc_hostetler_offline_vs_GLERL.pdf")), bbox_inches="tight")
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 main_compare_max_yearly_ice_conc():
"""
ice concentration
"""
var_name = ""
start_year = 1979
end_year = 1985
SimConfig = namedtuple("SimConfig", "path label")
base_config = SimConfig("/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012", "ERAI-driven")
modif_config = SimConfig("/home/huziy/skynet3_rech1/one_way_coupled_nemo_outputs_1979_1985", "CRCM5")
nemo_manager_base = NemoYearlyFilesManager(folder=base_config.path, suffix="icemod.nc")
nemo_manager_modif = NemoYearlyFilesManager(folder=modif_config.path, suffix="icemod.nc")
icecov_base, icecov_ts_base = nemo_manager_base.get_max_yearly_ice_fraction(start_year=start_year,
end_year=end_year)
icecov_modif, icecov_ts_modif = nemo_manager_modif.get_max_yearly_ice_fraction(start_year=start_year,
end_year=end_year)
lons, lats, bmp = nemo_manager_base.get_coords_and_basemap()
xx, yy = bmp(lons.copy(), lats.copy())
# Plot as usual: model, obs, model - obs
img_folder = Path("nemo/{}vs{}".format(modif_config.label, base_config.label))
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath("compare_yearmax_icecov_{}_vs_{}_{}-{}.pdf".format(
modif_config.label, base_config.label, start_year, end_year))
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("jet", 10)
diff_cmap = cm.get_cmap("RdBu_r", 10)
# base
ax = fig.add_subplot(gs[0, 0])
cs = bmp.contourf(xx, yy, icecov_base, cmap=cmap)
bmp.drawcoastlines(ax=ax)
ax.set_title(base_config.label)
# modif
ax = fig.add_subplot(gs[0, 1])
cs = bmp.contourf(xx, yy, icecov_modif, cmap=cmap, levels=cs.levels)
plt.colorbar(cs, cax=fig.add_subplot(gs[0, -1]))
bmp.drawcoastlines(ax=ax)
ax.set_title(modif_config.label)
# difference
ax = fig.add_subplot(gs[1, :])
cs = bmp.contourf(xx, yy, icecov_modif - icecov_base, cmap=diff_cmap, levels=np.arange(-1, 1.2, 0.2))
bmp.colorbar(cs, ax=ax)
bmp.drawcoastlines(ax=ax)
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
ax.set_title("{}-{}".format(modif_config.label, base_config.label))
plt.close(fig)
# Plot time series
img_file = img_folder.joinpath("ts_compare_yearmax_icecov_{}_vs_{}_{}-{}.pdf".format(
modif_config.label, base_config.label, start_year, end_year))
fig = plt.figure()
plt.plot(range(start_year, end_year + 1), icecov_ts_base, "b", lw=2, label=base_config.label)
plt.plot(range(start_year, end_year + 1), icecov_ts_modif, "r", lw=2, label=modif_config.label)
plt.legend()
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.grid()
plt.xlabel("Year")
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
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=2009):
crcm5_model_manager = Crcm5ModelDataManager(samples_folder_path=hl_data_path, all_files_in_samples_folder=True)
varname = "sohefldo"
season = "Summer"
season_to_months = {season: range(6, 9)}
season_months = list(season_to_months[season])
# Get Nemo manager here only for coordinates and mask
nemo_offline_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
suffix="grid_T.nc")
nemo_crcm5_manager = NemoYearlyFilesManager(
folder="/home/huziy/skynet3_rech1/one_way_coupled_nemo_outputs_1979_1985",
suffix="grid_T.nc")
lon2d, lat2d, bmp = nemo_offline_manager.get_coords_and_basemap()
# Interpolate
# get nemo sst
nemo_offline_sst = nemo_offline_manager.get_seasonal_clim_field(start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
varname=varname)
nemo_crcm5_sst = nemo_crcm5_manager.get_seasonal_clim_field(start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
varname=varname)
nemo_offline_sst = np.ma.masked_where(~nemo_offline_manager.lake_mask, nemo_offline_sst[season])
nemo_crcm5_sst = np.ma.masked_where(~nemo_offline_manager.lake_mask, nemo_crcm5_sst[season])
# plt.figure()
xx, yy = bmp(lon2d.copy(), lat2d.copy())
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
vmin = min(nemo_offline_sst.min(), nemo_crcm5_sst.min())
vmax = max(nemo_offline_sst.max(), nemo_crcm5_sst.max())
# 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")
if not img_folder.is_dir():
img_folder.mkdir()
img_file = img_folder.joinpath("{}_{}_nemo-offline_vs_nemo-crcm5_{}-{}.png".format(season.lower(),
varname, start_year, end_year))
fig = plt.figure()
gs = GridSpec(1, 3, width_ratios=[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("NEMO+CRCM5")
bmp.pcolormesh(xx, yy, nemo_crcm5_sst, 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_offline_sst, 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_yearmax_ice_cover_from_hostetler_with_glerl(
path="/home/huziy/skynet3_rech1/CRCM_GL_simulation/all_files",
start_year=2003,
end_year=2009):
model_manager = Crcm5ModelDataManager(samples_folder_path=path,
all_files_in_samples_folder=True)
varname = "LC"
hl_icecov_yearly_max = model_manager.get_yearmax_fields(
start_year=start_year, end_year=end_year, var_name=varname)
hl_icecov_yearly_max_clim = np.mean(list(hl_icecov_yearly_max.values()),
axis=0)
# 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_icecov_yearly_max_clim = model_manager.interpolate_data_to(
hl_icecov_yearly_max_clim, lon2d, lat2d)
hl_icecov_yearly_max_clim = np.ma.masked_where(~nemo_manager.lake_mask,
hl_icecov_yearly_max_clim)
model_lake_avg_ts = []
for the_year in range(start_year, end_year + 1):
model_lake_avg_ts.append(
hl_icecov_yearly_max[the_year][nemo_manager.lake_mask].mean())
# plt.figure()
xx, yy = bmp(lon2d.copy(), lat2d.copy())
# im = bmp.pcolormesh(xx, yy, model_yearmax_ice_conc)
# bmp.colorbar(im)
# Read and interpolate obs
path_to_obs = "/RESCUE/skynet3_rech1/huziy/nemo_obs_for_validation/glerl_icecov.nc"
obs_varname = "ice_cover"
obs_lake_avg_ts = []
with Dataset(path_to_obs) as ds:
time_var = ds.variables["time"]
lons_obs = ds.variables["lon"][:]
lats_obs = ds.variables["lat"][:]
dates = num2date(time_var[:], time_var.units)
nx, ny = lons_obs.shape
data = ds.variables[obs_varname][:]
data = np.ma.masked_where((data > 100) | (data < 0), data)
print(data.min(), data.max())
panel = pd.Panel(data=data,
items=dates,
major_axis=range(nx),
minor_axis=range(ny))
panel = panel.select(lambda d: start_year <= d.year <= end_year)
the_max_list = []
for key, g in panel.groupby(lambda d: d.year, axis="items"):
the_max_field = np.ma.max(np.ma.masked_where(
(g.values > 100) | (g.values < 0), g.values),
axis=0)
obs_lake_avg_ts.append(the_max_field.mean())
the_max_list.append(the_max_field)
obs_yearmax_ice_conc = np.ma.mean(the_max_list, axis=0) / 100.0
xs, ys, zs = lat_lon.lon_lat_to_cartesian(lons_obs.flatten(),
lats_obs.flatten())
ktree = cKDTree(list(zip(xs, ys, zs)))
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lon2d.flatten(),
lat2d.flatten())
dists, inds = ktree.query(list(zip(xt, yt, zt)))
obs_yearmax_ice_conc_interp = obs_yearmax_ice_conc.flatten(
)[inds].reshape(lon2d.shape)
obs_yearmax_ice_conc_interp = np.ma.masked_where(
~nemo_manager.lake_mask, obs_yearmax_ice_conc_interp)
# 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_yearmax_icecov_hl_vs_glerl_{}-{}.pdf".format(
start_year, end_year))
fig = plt.figure()
gs = GridSpec(2, 4, width_ratios=[1, 1, 1, 0.05])
all_axes = []
cmap = cm.get_cmap("jet", 10)
diff_cmap = cm.get_cmap("RdBu_r", 10)
# Model
ax = fig.add_subplot(gs[0, 0])
ax.set_title("Hostetler")
bmp.pcolormesh(xx,
yy,
hl_icecov_yearly_max_clim,
cmap=cmap,
vmin=0,
vmax=1)
all_axes.append(ax)
# Obs
ax = fig.add_subplot(gs[0, 2])
ax.set_title("GLERL")
im = bmp.pcolormesh(xx,
yy,
obs_yearmax_ice_conc_interp,
cmap=cmap,
vmin=0,
vmax=1)
all_axes.append(ax)
plt.colorbar(im, cax=fig.add_subplot(gs[0, -1]))
# Biases
ax = fig.add_subplot(gs[1, :])
ax.set_title("Hostetler - GLERL")
im = bmp.pcolormesh(xx,
yy,
hl_icecov_yearly_max_clim -
obs_yearmax_ice_conc_interp,
cmap=diff_cmap,
vmin=-1,
vmax=1)
bmp.colorbar(im, ax=ax)
all_axes.append(ax)
for the_ax in all_axes:
bmp.drawcoastlines(ax=the_ax)
fig.savefig(str(img_file), bbox_inches="tight")
plt.close(fig)
# Plot lake aversged ice concentrations
fig = plt.figure()
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.plot(range(start_year, end_year + 1),
model_lake_avg_ts,
"b",
lw=2,
label="Hostetler")
plt.plot(range(start_year, end_year + 1),
np.asarray(obs_lake_avg_ts) / 100.0,
"r",
lw=2,
label="GLERL")
plt.grid()
plt.legend(loc=3)
fig.savefig(str(
img_folder.joinpath(
"lake_avg_iceconc_hostetler_offline_vs_GLERL.pdf")),
bbox_inches="tight")
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=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 main_compare_max_yearly_ice_conc():
"""
ice concentration
"""
var_name = ""
start_year = 1979
end_year = 1985
SimConfig = namedtuple("SimConfig", "path label")
base_config = SimConfig(
"/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
"ERAI-driven")
modif_config = SimConfig(
"/home/huziy/skynet3_rech1/one_way_coupled_nemo_outputs_1979_1985",
"CRCM5")
nemo_manager_base = NemoYearlyFilesManager(folder=base_config.path,
suffix="icemod.nc")
nemo_manager_modif = NemoYearlyFilesManager(folder=modif_config.path,
suffix="icemod.nc")
icecov_base, icecov_ts_base = nemo_manager_base.get_max_yearly_ice_fraction(
start_year=start_year, end_year=end_year)
icecov_modif, icecov_ts_modif = nemo_manager_modif.get_max_yearly_ice_fraction(
start_year=start_year, end_year=end_year)
lons, lats, bmp = nemo_manager_base.get_coords_and_basemap()
xx, yy = bmp(lons.copy(), lats.copy())
# Plot as usual: model, obs, model - obs
img_folder = Path("nemo/{}vs{}".format(modif_config.label,
base_config.label))
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath(
"compare_yearmax_icecov_{}_vs_{}_{}-{}.pdf".format(
modif_config.label, base_config.label, start_year, end_year))
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("jet", 10)
diff_cmap = cm.get_cmap("RdBu_r", 10)
# base
ax = fig.add_subplot(gs[0, 0])
cs = bmp.contourf(xx, yy, icecov_base, cmap=cmap)
bmp.drawcoastlines(ax=ax)
ax.set_title(base_config.label)
# modif
ax = fig.add_subplot(gs[0, 1])
cs = bmp.contourf(xx, yy, icecov_modif, cmap=cmap, levels=cs.levels)
plt.colorbar(cs, cax=fig.add_subplot(gs[0, -1]))
bmp.drawcoastlines(ax=ax)
ax.set_title(modif_config.label)
# difference
ax = fig.add_subplot(gs[1, :])
cs = bmp.contourf(xx,
yy,
icecov_modif - icecov_base,
cmap=diff_cmap,
levels=np.arange(-1, 1.2, 0.2))
bmp.colorbar(cs, ax=ax)
bmp.drawcoastlines(ax=ax)
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
ax.set_title("{}-{}".format(modif_config.label, base_config.label))
plt.close(fig)
# Plot time series
img_file = img_folder.joinpath(
"ts_compare_yearmax_icecov_{}_vs_{}_{}-{}.pdf".format(
modif_config.label, base_config.label, start_year, end_year))
fig = plt.figure()
plt.plot(range(start_year, end_year + 1),
icecov_ts_base,
"b",
lw=2,
label=base_config.label)
plt.plot(range(start_year, end_year + 1),
icecov_ts_modif,
"r",
lw=2,
label=modif_config.label)
plt.legend()
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.grid()
plt.xlabel("Year")
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
def main():
# path_to_folder = "/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012"
path_to_folder = "/home/huziy/skynet3_rech1/one_way_coupled_nemo_outputs_1979_1985"
season_to_months = OrderedDict([
# ("Winter", (12, 1, 2)),
# ("Spring", (3, 4, 5)),
# ("Summer", (6, 7, 8)),
("Fall", (9, 10, 11))
])
start_year = 1990
end_year = 2010
u_manager = NemoYearlyFilesManager(folder=path_to_folder, suffix="_U.nc")
v_manager = NemoYearlyFilesManager(folder=path_to_folder, suffix="_V.nc")
u_clim = u_manager.get_seasonal_clim_field(start_year=start_year, end_year=end_year,
season_to_months=season_to_months, varname="vozocrtx")
v_clim = v_manager.get_seasonal_clim_field(start_year=start_year, end_year=end_year,
season_to_months=season_to_months, varname="vomecrty")
lons, lats, bmp = u_manager.get_coords_and_basemap()
lons, lats, rpole = u_manager.get_cartopy_proj_and_coords()
img_folder = Path("nemo/circulation")
if not img_folder.is_dir():
img_folder.mkdir()
fig = plt.figure()
gs = GridSpec(len(season_to_months), 1, width_ratios=[1, 0.05])
xx, yy = bmp(lons.copy(), lats.copy())
lons_1d = lons.mean(axis=0)
lats_1d = lats.mean(axis=1)
import cartopy.crs as ccrs
tr_xy = rpole.transform_points(ccrs.Geodetic(), lons, lats)
xx = tr_xy[:, :, 0]
yy = tr_xy[:, :, 1]
for i, season in enumerate(season_to_months):
ax = fig.add_subplot(gs[i, 0], projection=rpole)
u = u_clim[season]
v = v_clim[season]
# im = bmp.contourf(xx, yy, (u ** 2 + v ** 2) ** 0.5)
# bmp.colorbar(im)
# bmp.quiver(xx, yy, u, v, ax=ax)
# uproj, vproj, xx, yy = bmp.transform_vector(u, v, lons_1d, lats_1d, lons.shape[0], lons.shape[1],
# returnxy=True, masked=True)
# uproj, vproj = bmp.rotate_vector(u, v, lons.copy(), lats.copy())
c = (u ** 2 + v ** 2) ** 0.5 * 100
print(c.shape, u.shape, v.shape)
# Convert to cm
u *= 100
v *= 100
# u = np.ma.masked_where(u > 10, u)
# v = np.ma.masked_where(v > 10, v)
step = 2
q = ax.quiver(xx[::step, ::step], yy[::step, ::step], u[::step, ::step], v[::step, ::step], width=0.001,
pivot="middle", headwidth=5, headlength=5, scale=100)
qk = ax.quiverkey(q, 0.1, 0.1, 10, r'$10 \frac{cm}{s}$', fontproperties={'weight': 'bold'}, linewidth=1)
# ax.streamplot(xx, yy, u, v, linewidth=2, density=5, color="k")
# im = ax.pcolormesh(xx, yy, c)
# plt.colorbar(im, ax=ax)
ax.set_extent([xx[0, 0], xx[-1, -1], yy[0, 0], yy[-1, -1]], crs=rpole)
ax.coastlines("10m")
ax.add_feature(cfeature.NaturalEarthFeature('physical', 'lakes', '50m',
edgecolor='k',
facecolor="none"), linewidth=0.5)
ax.add_feature(cfeature.RIVERS, edgecolor="k", facecolor="none", linewidth=0.5)
ax.grid()
ax.set_title(season)
fig.savefig(str(img_folder.joinpath(
"NEMO-CRCM5-seasonal_surf_circulation_{}_arrows.pdf".format("_".join(season_to_months.keys())))),
bbox_inches="tight")
def main_plot_all_temp_profiles_in_one_figure(
folder_path="/home/huziy/skynet3_rech1/nemo_obs_for_validation/data_from_Ram_Yerubandi/T-profiles"
):
"""
figure layout:
Obs Model (Model - Obs)
p1
p2
...
pn
Point Positions map
:param folder_path: Path to the text files containing observed temperature profiles
"""
folder_path = os.path.expanduser(folder_path)
temperature_profile_file_prefixes = [
"08-01T-004A024.120.290",
# "08-01T-013A054.120.290", "08-00T-012A017.105.317",
"08-00T-004A177.106.286"
]
# nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012")
# 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")
# nemo_manager = NemoYearlyFilesManager(folder="/BIG1/huziy/CRCM5_NEMO_coupled_sim_nemo_outputs/NEMO")
#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")
# 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")
# nemo_manager = NemoYearlyFilesManager(
# folder="/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_30min_1980-2010")
nemo_manager = NemoYearlyFilesManager(
folder=
"/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/Simulations/cc_canesm2_nemo_offline_gathered_corrected_from_guillimin"
)
nemo_manager.get_coords_and_basemap(resolution="i")
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=35,
height_cm=30)
fig = plt.figure()
gs = GridSpec(len(temperature_profile_file_prefixes) + 2,
4,
width_ratios=[1, 1, 1, 0.05],
height_ratios=len(temperature_profile_file_prefixes) * [
1.0,
] + [
0.05,
2,
],
top=0.90,
wspace=0.2,
hspace=0.2)
color_levels = np.arange(0, 30, 0.5)
diff_levels = np.arange(-7, 8, 2)
diff_cmap = cm.get_cmap("bwr", len(diff_levels) - 1)
axes_list = []
imvalues = None
imdiff = None
titles = ["Obs", "Model", "Model - Obs"]
labels = [
"P{}".format(p) for p in range(len(temperature_profile_file_prefixes))
]
nupper_levs_to_plot = 5
obs_point_list = []
start_date, end_date = None, None
for row, prefix in enumerate(temperature_profile_file_prefixes):
# Get the data for plots
po = obs.get_profile_for_prefix(prefix, folder=folder_path)
obs_point_list.append(po)
tto, zzo, obs_profile = po.get_tz_section_data()
start_date = po.get_start_date()
end_date = po.get_end_date()
tt, zz, model_profile_interp = nemo_manager.get_tz_crosssection_for_the_point(
lon=po.longitude,
lat=po.latitude,
zlist=po.levels,
var_name="votemper",
start_date=start_date,
end_date=end_date)
ttm, zzm, model_profile = nemo_manager.get_tz_crosssection_for_the_point(
lon=po.longitude,
lat=po.latitude,
zlist=None,
var_name="votemper",
start_date=start_date,
end_date=end_date)
print("Unique model levels: ", np.unique(zzm))
# obs
ax = fig.add_subplot(gs[row, 0])
ax.contourf(tto, zzo, obs_profile, levels=color_levels)
axes_list.append(ax)
ax.set_ylabel(labels[row])
common_ylims = ax.get_ylim()
# model (not interpolated)
ax = fig.add_subplot(gs[row, 1])
imvalues = ax.contourf(ttm, zzm, model_profile, levels=color_levels)
ax.set_ylim(common_ylims)
ax.yaxis.set_ticklabels([])
axes_list.append(ax)
# model profile (interpolated to the observation levels) - obs profile
ax = fig.add_subplot(gs[row, 2])
diff = model_profile_interp - obs_profile
imdiff = ax.contourf(tt[:, :nupper_levs_to_plot],
zz[:, :nupper_levs_to_plot],
diff[:, :nupper_levs_to_plot],
levels=diff_levels,
cmap=diff_cmap,
extend="both")
# ax.yaxis.set_ticklabels([])
axes_list.append(ax)
if not row:
for axi, title in zip(axes_list, titles):
axi.set_title(title)
title_suffix = " {}-{}".format(start_date.year, end_date.year) if start_date.year < end_date.year \
else " {}".format(start_date.year)
fig.suptitle("Temperature, " + title_suffix,
font_properties=FontProperties(weight="bold"))
# plot colorbars
cb = plt.colorbar(imvalues,
cax=fig.add_subplot(
gs[len(temperature_profile_file_prefixes), :2]),
orientation="horizontal")
plt.colorbar(imdiff,
cax=fig.add_subplot(
gs[:len(temperature_profile_file_prefixes), 3]))
# Format dates
dfmt = DateFormatter("%b")
for i, ax in enumerate(axes_list):
ax.xaxis.set_major_formatter(dfmt)
ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
ax.invert_yaxis()
for ax in axes_list[:-3]:
ax.xaxis.set_ticklabels([])
# Plot station positions
lons, lats, bmp = nemo_manager.get_coords_and_basemap(resolution="i")
ax = fig.add_subplot(gs[len(temperature_profile_file_prefixes) + 1, :-2])
for i, po, label in zip(list(range(len(obs_point_list))), obs_point_list,
labels):
xx, yy = bmp(po.longitude, po.latitude)
bmp.scatter(xx, yy, c="r")
multiplier = 0.5 if i % 2 else 1
if i > 1:
multiplier *= -4
ax.annotate(label,
xy=(xx, yy),
xytext=(-20 * multiplier, 20 * multiplier),
textcoords='offset points',
ha='right',
va='bottom',
font_properties=FontProperties(size=14),
bbox=dict(boxstyle='round,pad=0.5', fc='yellow'),
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0'))
bmp.drawcoastlines(linewidth=0.5, ax=ax)
# fig.tight_layout()
if not img_dir.exists():
img_dir.mkdir(parents=True)
img_path = img_dir / "T-profiles.pdf"
print("Saving plots to {}".format(img_path))
fig.savefig(str(img_path), transparent=True, bbox_inches="tight", dpi=300)
def main_plot_all_temp_profiles_in_one_figure(
folder_path="/home/huziy/skynet3_rech1/nemo_obs_for_validation/data_from_Ram_Yerubandi/T-profiles"):
"""
figure layout:
Obs Model (Model - Obs)
p1
p2
...
pn
Point Positions map
:param folder_path: Path to the text files containing observed temperature profiles
"""
folder_path = os.path.expanduser(folder_path)
temperature_profile_file_prefixes = [
"08-01T-004A024.120.290",
# "08-01T-013A054.120.290", "08-00T-012A017.105.317",
"08-00T-004A177.106.286"
]
# nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012")
# 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")
# nemo_manager = NemoYearlyFilesManager(folder="/BIG1/huziy/CRCM5_NEMO_coupled_sim_nemo_outputs/NEMO")
#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")
# 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")
# nemo_manager = NemoYearlyFilesManager(
# folder="/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/zdf_gls_dt_and_sbc_30min_1980-2010")
nemo_manager = NemoYearlyFilesManager(
folder="/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/Simulations/cc_canesm2_nemo_offline_gathered_corrected_from_guillimin")
nemo_manager.get_coords_and_basemap(resolution="i")
plot_utils.apply_plot_params(font_size=12, width_pt=None, width_cm=35, height_cm=30)
fig = plt.figure()
gs = GridSpec(len(temperature_profile_file_prefixes) + 2, 4, width_ratios=[1, 1, 1, 0.05],
height_ratios=len(temperature_profile_file_prefixes) * [1.0, ] + [0.05, 2, ], top=0.90,
wspace=0.2, hspace=0.2)
color_levels = np.arange(0, 30, 0.5)
diff_levels = np.arange(-7, 8, 2)
diff_cmap = cm.get_cmap("bwr", len(diff_levels) - 1)
axes_list = []
imvalues = None
imdiff = None
titles = ["Obs", "Model", "Model - Obs"]
labels = ["P{}".format(p) for p in range(len(temperature_profile_file_prefixes))]
nupper_levs_to_plot = 5
obs_point_list = []
start_date, end_date = None, None
for row, prefix in enumerate(temperature_profile_file_prefixes):
# Get the data for plots
po = obs.get_profile_for_prefix(prefix, folder=folder_path)
obs_point_list.append(po)
tto, zzo, obs_profile = po.get_tz_section_data()
start_date = po.get_start_date()
end_date = po.get_end_date()
tt, zz, model_profile_interp = nemo_manager.get_tz_crosssection_for_the_point(lon=po.longitude, lat=po.latitude,
zlist=po.levels,
var_name="votemper",
start_date=start_date,
end_date=end_date)
ttm, zzm, model_profile = nemo_manager.get_tz_crosssection_for_the_point(lon=po.longitude, lat=po.latitude,
zlist=None,
var_name="votemper",
start_date=start_date,
end_date=end_date)
print("Unique model levels: ", np.unique(zzm))
# obs
ax = fig.add_subplot(gs[row, 0])
ax.contourf(tto, zzo, obs_profile, levels=color_levels)
axes_list.append(ax)
ax.set_ylabel(labels[row])
common_ylims = ax.get_ylim()
# model (not interpolated)
ax = fig.add_subplot(gs[row, 1])
imvalues = ax.contourf(ttm, zzm, model_profile, levels=color_levels)
ax.set_ylim(common_ylims)
ax.yaxis.set_ticklabels([])
axes_list.append(ax)
# model profile (interpolated to the observation levels) - obs profile
ax = fig.add_subplot(gs[row, 2])
diff = model_profile_interp - obs_profile
imdiff = ax.contourf(tt[:, :nupper_levs_to_plot], zz[:, :nupper_levs_to_plot], diff[:, :nupper_levs_to_plot], levels=diff_levels, cmap=diff_cmap, extend="both")
# ax.yaxis.set_ticklabels([])
axes_list.append(ax)
if not row:
for axi, title in zip(axes_list, titles):
axi.set_title(title)
title_suffix = " {}-{}".format(start_date.year, end_date.year) if start_date.year < end_date.year \
else " {}".format(start_date.year)
fig.suptitle("Temperature, " + title_suffix, font_properties=FontProperties(weight="bold"))
# plot colorbars
cb = plt.colorbar(imvalues, cax=fig.add_subplot(gs[len(temperature_profile_file_prefixes), :2]), orientation="horizontal")
plt.colorbar(imdiff, cax=fig.add_subplot(gs[:len(temperature_profile_file_prefixes), 3]))
# Format dates
dfmt = DateFormatter("%b")
for i, ax in enumerate(axes_list):
ax.xaxis.set_major_formatter(dfmt)
ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
ax.invert_yaxis()
for ax in axes_list[:-3]:
ax.xaxis.set_ticklabels([])
# Plot station positions
lons, lats, bmp = nemo_manager.get_coords_and_basemap(resolution="i")
ax = fig.add_subplot(gs[len(temperature_profile_file_prefixes) + 1, :-2])
for i, po, label in zip(list(range(len(obs_point_list))), obs_point_list, labels):
xx, yy = bmp(po.longitude, po.latitude)
bmp.scatter(xx, yy, c="r")
multiplier = 0.5 if i % 2 else 1
if i > 1:
multiplier *= -4
ax.annotate(label, xy=(xx, yy), xytext=(-20 * multiplier, 20 * multiplier),
textcoords='offset points', ha='right', va='bottom',
font_properties=FontProperties(size=14),
bbox=dict(boxstyle='round,pad=0.5', fc='yellow'),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
bmp.drawcoastlines(linewidth=0.5, ax=ax)
# fig.tight_layout()
if not img_dir.exists():
img_dir.mkdir(parents=True)
img_path = img_dir / "T-profiles.pdf"
print("Saving plots to {}".format(img_path))
fig.savefig(str(img_path), transparent=True, bbox_inches="tight", dpi=300)
def main_plot_all_temp_profiles_in_one_figure(
folder_path="/home/huziy/skynet3_rech1/nemo_obs_for_validation/data_from_Ram_Yerubandi/T-profiles"):
"""
figure layout:
Obs Model (Model - Obs)
p1
p2
...
pn
Point Positions map
:param folder_path: Path to the text files containing observed temperature profiles
"""
folder_path = os.path.expanduser(folder_path)
temperature_profile_file_prefixes = [
"08-01T-004A024.120.290",
# "08-01T-013A054.120.290", "08-00T-012A017.105.317",
"08-00T-004A177.106.286"
]
nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012")
plot_utils.apply_plot_params(font_size=16, width_pt=None, width_cm=40, height_cm=30)
fig = plt.figure()
gs = GridSpec(len(temperature_profile_file_prefixes) + 1, 5, width_ratios=[1, 1, 0.05, 1, 0.05],
height_ratios=len(temperature_profile_file_prefixes) * [1.0, ] + [3, ], top=0.90,
wspace=0.4, hspace=0.2)
color_levels = np.arange(-1, 30, 1)
diff_levels = np.arange(-10, 10.5, 0.5)
diff_cmap = cm.get_cmap("RdBu_r", len(diff_levels) - 1)
axes_list = []
imvalues = None
imdiff = None
titles = ["Obs", "Model", "Model - Obs"]
labels = ["P{}".format(p) for p in range(len(temperature_profile_file_prefixes))]
obs_point_list = []
start_date, end_date = None, None
for row, prefix in enumerate(temperature_profile_file_prefixes):
# Get the data for plots
po = obs.get_profile_for_prefix(prefix, folder=folder_path)
obs_point_list.append(po)
tto, zzo, obs_profile = po.get_tz_section_data()
start_date = po.get_start_date()
end_date = po.get_end_date()
tt, zz, model_profile_interp = nemo_manager.get_tz_crosssection_for_the_point(lon=po.longitude, lat=po.latitude,
zlist=po.levels,
var_name="votemper",
start_date=start_date,
end_date=end_date)
ttm, zzm, model_profile = nemo_manager.get_tz_crosssection_for_the_point(lon=po.longitude, lat=po.latitude,
zlist=None,
var_name="votemper",
start_date=start_date,
end_date=end_date)
print("Unique model levels: ", np.unique(zzm))
# obs
ax = fig.add_subplot(gs[row, 0])
ax.contourf(tto, zzo, obs_profile, levels=color_levels)
axes_list.append(ax)
ax.set_ylabel(labels[row])
common_ylims = ax.get_ylim()
# model (not interpolated)
ax = fig.add_subplot(gs[row, 1])
imvalues = ax.contourf(ttm, zzm, model_profile, levels=color_levels)
ax.set_ylim(common_ylims)
ax.yaxis.set_ticklabels([])
axes_list.append(ax)
# model profile (interpolated to the observation levels) - obs profile
ax = fig.add_subplot(gs[row, 3])
diff = model_profile_interp - obs_profile
imdiff = ax.contourf(tt, zz, diff, levels=diff_levels, cmap=diff_cmap, extend="both")
ax.yaxis.set_ticklabels([])
axes_list.append(ax)
if not row:
for axi, title in zip(axes_list, titles):
axi.set_title(title)
title_suffix = " {}-{}".format(start_date.year, end_date.year) if start_date.year < end_date.year \
else " {}".format(start_date.year)
fig.suptitle("Temperature, " + title_suffix, font_properties=FontProperties(weight="bold"))
# plot colorbars
cb = plt.colorbar(imvalues, cax=fig.add_subplot(gs[:-1, 2]))
plt.colorbar(imdiff, cax=fig.add_subplot(gs[:-1, 4]))
# Format dates
dfmt = DateFormatter("%b")
for i, ax in enumerate(axes_list):
ax.xaxis.set_major_formatter(dfmt)
ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
ax.invert_yaxis()
for ax in axes_list[:-3]:
ax.xaxis.set_ticklabels([])
# Plot station positions
lons, lats, bmp = nemo_manager.get_coords_and_basemap()
ax = fig.add_subplot(gs[len(temperature_profile_file_prefixes), :-2])
for i, po, label in zip(list(range(len(obs_point_list))), obs_point_list, labels):
xx, yy = bmp(po.longitude, po.latitude)
bmp.scatter(xx, yy, c="r")
multiplier = 0.5 if i % 2 else 1
if i > 1:
multiplier *= -4
ax.annotate(label, xy=(xx, yy), xytext=(-20 * multiplier, 20 * multiplier),
textcoords='offset points', ha='right', va='bottom',
font_properties=FontProperties(size=14),
bbox=dict(boxstyle='round,pad=0.5', fc='yellow'),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
bmp.drawcoastlines(linewidth=0.5, ax=ax)
# plt.tight_layout()
img_path = "nemo/T-profiles.pdf"
fig.savefig(img_path, transparent=True, bbox_inches="tight")
