def main():
start_year = 1979
end_year = 2000
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
var_name_list = ["TT", "PR", "LC", "HR", "HU", "AV", "I5", "AL"]
season_to_months = commons.season_to_months
vname_to_level = {
"TT": 1, "PR": -1, "SN": -1, "LC": -1, "HR": 1, "HU": 1, "AV": -1, "I5": -1, "AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID, "PR": level_kinds.ARBITRARY, "SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY, "HR": level_kinds.HYBRID, "HU": level_kinds.HYBRID, "AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY, "AL": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
vname = "LC"
common_params = dict(start_year=start_year, end_year=end_year,
filename_prefix=vname_to_file_prefix[vname], level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname], varname=vname, mask=avg_mask)
hl_icefrac = get_area_avg_timeseries(sim_label_to_path[HL_LABEL], **common_params)
nemo_icefrac = get_area_avg_timeseries(sim_label_to_path[NEMO_LABEL], **common_params)
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
var_name_list = ["TT", "LC", "HU", "AV", "AL"]
vname_to_level = {
"TT": 1,
"PR": -1,
"SN": -1,
"LC": -1,
"HR": 1,
"HU": 1,
"AV": -1,
"I5": -1,
"AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID,
"PR": level_kinds.ARBITRARY,
"SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY,
"HR": level_kinds.HYBRID,
"HU": level_kinds.HYBRID,
"AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY,
"AL": level_kinds.ARBITRARY,
"J8": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
current_label = HL_LABEL
vname_to_ts = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year,
end_year=end_year,
filename_prefix=vname_to_file_prefix[vname],
level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname],
varname=vname,
mask=avg_mask)
ts = get_area_avg_timeseries(sim_label_to_path[current_label],
**common_params)
vname_to_ts[vname] = ts
plot_utils.apply_plot_params(font_size=10, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts), 1)
row = 0
for vname, ts in vname_to_ts.items():
ax = fig.add_subplot(gs[row, 0])
ts.plot(lw=2, color="k", ax=ax)
ax.set_ylabel(vname)
row += 1
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
fig.tight_layout()
fig.savefig(os.path.join(
img_folder, "{}_lakeand_air_props_ts_over_GL_{}-{}.png".format(
current_label, start_year, end_year)),
dpi=commons.dpi,
transparent=True)
def main():
start_year = 1979
end_year = 1988
img_folder = "nemo_vs_hostetler_GL_extended_domain"
# create the image folder if necessary
img_folder_p = Path(img_folder)
if not img_folder_p.is_dir():
img_folder_p.mkdir()
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
# Older, shorter [1971 - 1981], smaller domain simulations
# sim_label_to_path = OrderedDict(
# [(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
# (NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
# )
sim_label_to_path = OrderedDict([(
HL_LABEL,
"/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected"
), (NEMO_LABEL, "/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/Samples")])
var_name_list = [
"TT",
"PR",
]
vname_to_level = {
"TT": 1,
"PR": -1,
"SN": -1,
"LC": -1,
"HR": 1,
"HU": 1,
"AV": -1,
"I5": -1,
"AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID,
"PR": level_kinds.ARBITRARY,
"SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY,
"HR": level_kinds.HYBRID,
"HU": level_kinds.HYBRID,
"AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY,
"AL": level_kinds.ARBITRARY,
"J8": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
nemo_lake_mask, mask_lons, mask_lats = get_nemo_lakes_mask(
samples_dir=sim_label_to_path[NEMO_LABEL])
path_to_erai_data = "/RESCUE/skynet3_rech1/huziy/ERA-Interim_0.75_NEMO_pilot/"
vname_to_ts_hl = OrderedDict()
vname_to_ts_erai = OrderedDict()
vname_to_ts_nemo = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year,
end_year=end_year,
filename_prefix=vname_to_file_prefix[vname],
level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname],
varname=vname,
mask=nemo_lake_mask,
mask_lons2d=mask_lons,
mask_lats2d=mask_lats)
current_label = HL_LABEL
vname_to_ts_hl[vname], _, _ = get_area_avg_timeseries(
sim_label_to_path[current_label],
file_per_var=True,
**common_params)
current_label = NEMO_LABEL
vname_to_ts_nemo[vname], lons2d, lats2d = get_area_avg_timeseries(
sim_label_to_path[current_label], **common_params)
vname_to_ts_erai[vname] = get_area_avg_from_erai_data(
start_year=start_year,
end_year=end_year,
var_folder=os.path.join(path_to_erai_data, vname),
varname=vname,
mask=nemo_lake_mask,
mask_lons=lons2d,
mask_lats=lats2d)
plot_utils.apply_plot_params(font_size=12, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts_hl), 1)
plot_monthly_model_outputs = False
for row, vname in enumerate(vname_to_ts_hl):
ax = fig.add_subplot(gs[row, 0])
ax.set_ylabel(vname)
coef = 1
coef_erai = 1
if vname == "PR":
coef = 24 * 3600 * 1000
coef_erai = coef / 1000.0 # Already in mm/s
plot_monthly_model_outputs = True
ts = vname_to_ts_hl[vname].groupby(lambda d: datetime(
d.year, d.month, 15
if plot_monthly_model_outputs else d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(lambda d: datetime(
d.year, d.month, 15
if plot_monthly_model_outputs else d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
ts = vname_to_ts_erai[vname].groupby(
lambda d: datetime(d.year, d.month, 15)).mean() * coef_erai
ax.plot(ts.index, ts.values, lw=2, color="k", label="ERA-Interim")
if row == 0:
ax.legend(loc="lower right")
ax.grid()
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig(os.path.join(
img_folder,
"{}-{}_validate_{}_{}_ts_with_erai.png".format(start_year, end_year,
HL_LABEL, NEMO_LABEL)),
transparent=True,
dpi=400)
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
# Older, shorter [1971 - 1981], smaller domain simulations
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
var_name_list = ["TT", "HU"]
vname_to_level = {
"TT": 1,
"PR": -1,
"SN": -1,
"LC": -1,
"HR": 1,
"HU": 1,
"AV": -1,
"I5": -1,
"AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID,
"PR": level_kinds.ARBITRARY,
"SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY,
"HR": level_kinds.HYBRID,
"HU": level_kinds.HYBRID,
"AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY,
"AL": level_kinds.ARBITRARY,
"J8": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
# extend the mask
dn = 40
marginx = dn // 2
marginy = dn // 2
nx_ext, ny_ext = [(n + dn) for n in avg_mask.shape]
print([(n + dn) for n in avg_mask.shape])
avg_mask_ext = np.zeros((nx_ext, ny_ext), dtype=np.bool)
avg_mask_ext[marginx:-marginx, marginy:-marginy] = avg_mask
path_to_erai_data = "/RESCUE/skynet3_rech1/huziy/ERA-Interim_0.75_NEMO_pilot/"
vname_to_ts_hl = OrderedDict()
vname_to_ts_erai = OrderedDict()
vname_to_ts_nemo = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year,
end_year=end_year,
filename_prefix=vname_to_file_prefix[vname],
level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname],
varname=vname,
mask=avg_mask)
current_label = HL_LABEL
vname_to_ts_hl[vname] = get_area_avg_timeseries(
sim_label_to_path[current_label], **common_params)
current_label = NEMO_LABEL
vname_to_ts_nemo[vname] = get_area_avg_timeseries(
sim_label_to_path[current_label], **common_params)
vname_to_ts_erai[vname] = get_area_avg_from_erai_data(
start_year=start_year,
end_year=end_year,
var_folder=os.path.join(os.path.join(path_to_erai_data, vname)),
varname=vname,
mask=avg_mask_ext)
plot_utils.apply_plot_params(font_size=12, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts_hl), 1)
for row, vname in enumerate(vname_to_ts_hl):
ax = fig.add_subplot(gs[row, 0])
ax.set_ylabel(vname)
coef = 1
coef_erai = 1
# plot monthly means for precipitations
if vname == "PR":
coef = 24 * 3600 * 1000
coef_erai = coef / 1000.0 # Already in mm/s
ts = vname_to_ts_hl[vname].groupby(
lambda d: datetime(d.year, d.month, 15)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(
lambda d: datetime(d.year, d.month, 15)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
else:
ts = vname_to_ts_hl[vname].groupby(
lambda d: datetime(d.year, d.month, d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(
lambda d: datetime(d.year, d.month, d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
ts = vname_to_ts_erai[vname].groupby(
lambda d: datetime(d.year, d.month, 15)).mean() * coef_erai
ax.plot(ts.index, ts.values, lw=2, color="k", label="ERA-Interim")
if row == 0:
ax.legend()
ax.grid()
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig(os.path.join(
img_folder,
"{}-{}_validate_{}_{}_ts_with_erai.png".format(start_year, end_year,
HL_LABEL, NEMO_LABEL)),
transparent=True,
dpi=400)
def main():
start_year = 1979
end_year = 2000
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
var_name_list = ["TT", "PR", "LC", "HR", "HU", "AV", "I5", "AL"]
season_to_months = commons.season_to_months
vname_to_level = {
"TT": 1,
"PR": -1,
"SN": -1,
"LC": -1,
"HR": 1,
"HU": 1,
"AV": -1,
"I5": -1,
"AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID,
"PR": level_kinds.ARBITRARY,
"SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY,
"HR": level_kinds.HYBRID,
"HU": level_kinds.HYBRID,
"AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY,
"AL": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
vname = "LC"
common_params = dict(start_year=start_year,
end_year=end_year,
filename_prefix=vname_to_file_prefix[vname],
level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname],
varname=vname,
mask=avg_mask)
hl_icefrac = get_area_avg_timeseries(sim_label_to_path[HL_LABEL],
**common_params)
nemo_icefrac = get_area_avg_timeseries(sim_label_to_path[NEMO_LABEL],
**common_params)
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
# Older, shorter [1971 - 1981], smaller domain simulations
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
var_name_list = ["TT", "HU"]
vname_to_level = {
"TT": 1, "PR": -1, "SN": -1, "LC": -1, "HR": 1, "HU": 1, "AV": -1, "I5": -1, "AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID, "PR": level_kinds.ARBITRARY, "SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY, "HR": level_kinds.HYBRID, "HU": level_kinds.HYBRID, "AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY, "AL": level_kinds.ARBITRARY, "J8": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
# extend the mask
dn = 40
marginx = dn // 2
marginy = dn // 2
nx_ext, ny_ext = [(n + dn) for n in avg_mask.shape]
print([(n + dn) for n in avg_mask.shape])
avg_mask_ext = np.zeros((nx_ext, ny_ext), dtype=np.bool)
avg_mask_ext[marginx:-marginx, marginy:-marginy] = avg_mask
path_to_erai_data = "/RESCUE/skynet3_rech1/huziy/ERA-Interim_0.75_NEMO_pilot/"
vname_to_ts_hl = OrderedDict()
vname_to_ts_erai = OrderedDict()
vname_to_ts_nemo = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year, end_year=end_year,
filename_prefix=vname_to_file_prefix[vname], level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname], varname=vname, mask=avg_mask)
current_label = HL_LABEL
vname_to_ts_hl[vname] = get_area_avg_timeseries(sim_label_to_path[current_label], **common_params)
current_label = NEMO_LABEL
vname_to_ts_nemo[vname] = get_area_avg_timeseries(sim_label_to_path[current_label], **common_params)
vname_to_ts_erai[vname] = get_area_avg_from_erai_data(start_year=start_year, end_year=end_year,
var_folder=os.path.join(os.path.join(path_to_erai_data, vname)),
varname=vname, mask=avg_mask_ext)
plot_utils.apply_plot_params(font_size=12, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts_hl), 1)
for row, vname in enumerate(vname_to_ts_hl):
ax = fig.add_subplot(gs[row, 0])
ax.set_ylabel(vname)
coef = 1
coef_erai = 1
# plot monthly means for precipitations
if vname == "PR":
coef = 24 * 3600 * 1000
coef_erai = coef / 1000.0 # Already in mm/s
ts = vname_to_ts_hl[vname].groupby(lambda d: datetime(d.year, d.month, 15)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(lambda d: datetime(d.year, d.month, 15)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
else:
ts = vname_to_ts_hl[vname].groupby(lambda d: datetime(d.year, d.month, d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(lambda d: datetime(d.year, d.month, d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
ts = vname_to_ts_erai[vname].groupby(lambda d: datetime(d.year, d.month, 15)).mean() * coef_erai
ax.plot(ts.index, ts.values, lw=2, color="k", label="ERA-Interim")
if row == 0:
ax.legend()
ax.grid()
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "{}-{}_validate_{}_{}_ts_with_erai.png".format(start_year, end_year, HL_LABEL, NEMO_LABEL)), transparent=True, dpi=400)
def main():
start_year = 1979
end_year = 1981
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
var_name_list = ["TT", "LC", "HU", "AV", "AL"]
vname_to_level = {
"TT": 1, "PR": -1, "SN": -1, "LC": -1, "HR": 1, "HU": 1, "AV": -1, "I5": -1, "AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID, "PR": level_kinds.ARBITRARY, "SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY, "HR": level_kinds.HYBRID, "HU": level_kinds.HYBRID, "AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY, "AL": level_kinds.ARBITRARY, "J8":level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
current_label = HL_LABEL
vname_to_ts = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year, end_year=end_year,
filename_prefix=vname_to_file_prefix[vname], level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname], varname=vname, mask=avg_mask)
ts = get_area_avg_timeseries(sim_label_to_path[current_label], **common_params)
vname_to_ts[vname] = ts
plot_utils.apply_plot_params(font_size=10, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts), 1)
row = 0
for vname, ts in vname_to_ts.items():
ax = fig.add_subplot(gs[row, 0])
ts.plot(lw=2, color="k", ax=ax)
ax.set_ylabel(vname)
row += 1
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "{}_lakeand_air_props_ts_over_GL_{}-{}.png".format(current_label, start_year, end_year)), dpi=commons.dpi, transparent=True)
def main():
start_year = 1979
end_year = 1988
img_folder = "nemo_vs_hostetler_GL_extended_domain"
# create the image folder if necessary
img_folder_p = Path(img_folder)
if not img_folder_p.is_dir():
img_folder_p.mkdir()
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
# Older, shorter [1971 - 1981], smaller domain simulations
# sim_label_to_path = OrderedDict(
# [(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
# (NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
# )
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected"),
(NEMO_LABEL, "/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/Samples")]
)
var_name_list = ["TT",
"PR"
]
vname_to_level = {
"TT": 1, "PR": -1, "SN": -1, "LC": -1, "HR": 1, "HU": 1, "AV": -1, "I5": -1, "AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID, "PR": level_kinds.ARBITRARY, "SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY, "HR": level_kinds.HYBRID, "HU": level_kinds.HYBRID, "AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY, "AL": level_kinds.ARBITRARY, "J8": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm",
"J8": "pm"
}
# ---> ---->
nemo_lake_mask, mask_lons, mask_lats = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
path_to_erai_data = "/RESCUE/skynet3_rech1/huziy/ERA-Interim_0.75_NEMO_pilot/"
vname_to_ts_hl = OrderedDict()
vname_to_ts_erai = OrderedDict()
vname_to_ts_nemo = OrderedDict()
for vname in var_name_list:
common_params = dict(start_year=start_year, end_year=end_year,
filename_prefix=vname_to_file_prefix[vname], level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname], varname=vname,
mask=nemo_lake_mask, mask_lons2d=mask_lons, mask_lats2d=mask_lats)
current_label = HL_LABEL
vname_to_ts_hl[vname], _, _ = get_area_avg_timeseries(sim_label_to_path[current_label],
file_per_var=True, **common_params)
current_label = NEMO_LABEL
vname_to_ts_nemo[vname], lons2d, lats2d = get_area_avg_timeseries(sim_label_to_path[current_label],
**common_params)
vname_to_ts_erai[vname] = get_area_avg_from_erai_data(start_year=start_year, end_year=end_year,
var_folder=os.path.join(path_to_erai_data, vname),
varname=vname, mask=nemo_lake_mask,
mask_lons=lons2d, mask_lats=lats2d)
plot_utils.apply_plot_params(font_size=12, width_cm=20, height_cm=20)
fig = plt.figure()
gs = GridSpec(len(vname_to_ts_hl), 1)
plot_monthly_model_outputs = False
for row, vname in enumerate(vname_to_ts_hl):
ax = fig.add_subplot(gs[row, 0])
ax.set_ylabel(vname)
coef = 1
coef_erai = 1
if vname == "PR":
coef = 24 * 3600 * 1000
coef_erai = coef / 1000.0 # Already in mm/s
plot_monthly_model_outputs = True
ts = vname_to_ts_hl[vname].groupby(lambda d: datetime(d.year, d.month, 15 if plot_monthly_model_outputs else d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="b", label=HL_LABEL)
ts = vname_to_ts_nemo[vname].groupby(lambda d: datetime(d.year, d.month, 15 if plot_monthly_model_outputs else d.day)).mean() * coef
ax.plot(ts.index, ts.values, lw=2, color="r", label=NEMO_LABEL)
ts = vname_to_ts_erai[vname].groupby(lambda d: datetime(d.year, d.month, 15)).mean() * coef_erai
ax.plot(ts.index, ts.values, lw=2, color="k", label="ERA-Interim")
if row == 0:
ax.legend(loc="lower right")
ax.grid()
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig(os.path.join(img_folder,
"{}-{}_validate_{}_{}_ts_with_erai.png".format(start_year, end_year, HL_LABEL, NEMO_LABEL)),
transparent=True, dpi=400)
