def __obs_case(period, vname_to_level: dict, vname_map: dict, label: str, base_dir: str):
label_to_config = OrderedDict([(
label, {
DataManager.SP_BASE_FOLDER: str(base_dir),
DataManager.SP_DATASOURCE_TYPE: data_source_types.ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
DataManager.SP_INTERNAL_TO_INPUT_VNAME_MAPPING: vname_map,
DataManager.SP_LEVEL_MAPPING: vname_to_level,
DataManager.SP_OFFSET_MAPPING: vname_to_offset_CRCM5,
DataManager.SP_MULTIPLIER_MAPPING: vname_to_multiplier_CRCM5,
DataManager.SP_VARNAME_TO_FILENAME_PREFIX_MAPPING: vname_to_fname_prefix_CRCM5,
"out_folder": f"lake_effect_analysis_icefix_{label}_{period.start.year}-{period.end.year}"
}
)])
calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config, period=period, nprocs_to_use=15)
def main():
label = "Obs"
period = Period(datetime(1980, 11, 1), datetime(1981, 2, 1))
# should be continuous??
months_of_interest = [11, 12, 1]
period.months_of_interest = months_of_interest
vname_to_level_erai = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
vname_map = {}
vname_map.update(vname_map_CRCM5)
label_to_config = OrderedDict([(label, {
DataManager.SP_BASE_FOLDER:
"/HOME/huziy/skynet3_rech1/obs_data_for_HLES/interploated_to_the_same_grid/GL_0.1_452x260",
DataManager.SP_DATASOURCE_TYPE:
data_source_types.
ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
DataManager.SP_INTERNAL_TO_INPUT_VNAME_MAPPING:
vname_map,
DataManager.SP_LEVEL_MAPPING:
vname_to_level_erai,
DataManager.SP_OFFSET_MAPPING:
vname_to_offset_CRCM5,
DataManager.SP_MULTIPLIER_MAPPING:
vname_to_multiplier_CRCM5,
DataManager.SP_VARNAME_TO_FILENAME_PREFIX_MAPPING:
vname_to_fname_prefix_CRCM5,
"out_folder":
"lake_effect_analysis_icefix_{}_{}-{}_test1".format(
label, period.start.year, period.end.year)
})])
calculate_lake_effect_snowfall_each_year_in_parallel(
label_to_config=label_to_config, period=period, nprocs_to_use=20)
def main():
label = "Obs"
period = Period(
datetime(1980, 11, 1), datetime(1981, 2, 1)
)
# should be continuous??
months_of_interest = [11, 12, 1]
period.months_of_interest = months_of_interest
vname_to_level_erai = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
vname_map = {}
vname_map.update(vname_map_CRCM5)
label_to_config = OrderedDict([(
label, {
DataManager.SP_BASE_FOLDER: "/HOME/huziy/skynet3_rech1/obs_data_for_HLES/interploated_to_the_same_grid/GL_0.1_452x260",
DataManager.SP_DATASOURCE_TYPE: data_source_types.ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
DataManager.SP_INTERNAL_TO_INPUT_VNAME_MAPPING: vname_map,
DataManager.SP_LEVEL_MAPPING: vname_to_level_erai,
DataManager.SP_OFFSET_MAPPING: vname_to_offset_CRCM5,
DataManager.SP_MULTIPLIER_MAPPING: vname_to_multiplier_CRCM5,
DataManager.SP_VARNAME_TO_FILENAME_PREFIX_MAPPING: vname_to_fname_prefix_CRCM5,
"out_folder": "lake_effect_analysis_icefix_{}_{}-{}_test1".format(label, period.start.year, period.end.year)
}
)])
calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config, period=period, nprocs_to_use=20)
def __obs_case(period, vname_to_level: dict, vname_map: dict, label: str,
base_dir: str):
label_to_config = OrderedDict([(label, {
DataManager.SP_BASE_FOLDER:
str(base_dir),
DataManager.SP_DATASOURCE_TYPE:
data_source_types.
ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
DataManager.SP_INTERNAL_TO_INPUT_VNAME_MAPPING:
vname_map,
DataManager.SP_LEVEL_MAPPING:
vname_to_level,
DataManager.SP_OFFSET_MAPPING:
vname_to_offset_CRCM5,
DataManager.SP_MULTIPLIER_MAPPING:
vname_to_multiplier_CRCM5,
DataManager.SP_VARNAME_TO_FILENAME_PREFIX_MAPPING:
vname_to_fname_prefix_CRCM5,
"out_folder":
f"lake_effect_analysis_icefix_{label}_{period.start.year}-{period.end.year}"
})])
calculate_lake_effect_snowfall_each_year_in_parallel(
label_to_config=label_to_config, period=period, nprocs_to_use=15)
def main():
period = Period(datetime(1980, 12, 1), datetime(2009, 3, 1))
# should be consequent
months_of_interest = [11, 12, 1]
period.months_of_interest = months_of_interest
vname_to_level_erai = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
ERAI_label = "ERA-Interim"
label = ERAI_label
label_to_config = OrderedDict([ # ERA-Interim
(label, {
"base_folder":
"/RECH/data/Driving_data/Offline/ERA-Interim_0.75/6h_Analysis",
"data_source_type":
data_source_types.ALL_VARS_IN_A_FOLDER_OF_RPN_FILES,
"min_dt":
timedelta(hours=6),
"varname_mapping":
vname_map_CRCM5,
"level_mapping":
vname_to_level_erai,
"offset_mapping":
vname_to_offset_CRCM5,
"multiplier_mapping":
vname_to_multiplier_CRCM5,
"out_folder":
"lake_effect_analysis_{}_{}-{}".format(label, period.start.year,
period.end.year)
}),
# Add additional sources below
])
import time
t0 = time.time()
# calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config, period=period, nprocs_to_use=5)
print("Execution time: {} s".format(time.time() - t0))
# calculate_lake_effect_snowfall(label_to_config=label_to_config, period=period)
label = "CRCM5_NEMO_based_on_TT_PR"
vname_map = {}
vname_map.update(vname_map_CRCM5)
vname_map.update({default_varname_mappings.SNOWFALL_RATE: "XX"})
label_to_config_CRCM5 = OrderedDict([(label, {
"base_folder":
"/HOME/huziy/skynet3_rech1/CRCM5_outputs/coupled-GL-NEMO1h/selected_fields",
"data_source_type":
data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"min_dt":
timedelta(hours=3),
"varname_mapping":
vname_map,
"level_mapping":
vname_to_level_erai,
"offset_mapping":
vname_to_offset_CRCM5,
"multiplier_mapping":
vname_to_multiplier_CRCM5,
"out_folder":
"lake_effect_analysis_{}_{}-{}".format(label, period.start.year,
period.end.year)
})])
calculate_lake_effect_snowfall_each_year_in_parallel(
label_to_config=label_to_config_CRCM5, period=period, nprocs_to_use=16)
label = "CRCM5_Hostetler_based_on_TT_PR"
vname_map = {}
vname_map.update(vname_map_CRCM5)
vname_map.update({default_varname_mappings.SNOWFALL_RATE: "XX"})
label_to_config_CRCM5 = OrderedDict([(label, {
"base_folder":
"/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
"data_source_type":
data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"min_dt":
timedelta(hours=3),
"varname_mapping":
vname_map,
"level_mapping":
vname_to_level_erai,
"offset_mapping":
vname_to_offset_CRCM5,
"multiplier_mapping":
vname_to_multiplier_CRCM5,
"filename_prefix_mapping":
vname_to_fname_prefix_CRCM5,
"out_folder":
"lake_effect_analysis_{}_{}-{}".format(label, period.start.year,
period.end.year)
})])
def main():
period = Period(
datetime(1980, 12, 1), datetime(1985, 3, 1)
)
# should be consequent
months_of_interest = [11, 12, 1]
period.months_of_interest = months_of_interest
vname_to_level_erai = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
ERAI_label = "ERA-Interim"
label = ERAI_label
label_to_config = OrderedDict(
[ # ERA-Interim
(label,
{
"base_folder": "/RECH/data/Driving_data/Offline/ERA-Interim_0.75/6h_Analysis",
"data_source_type": data_source_types.ALL_VARS_IN_A_FOLDER_OF_RPN_FILES,
"min_dt": timedelta(hours=6),
"varname_mapping": vname_map_CRCM5,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
),
# Add additional sources below
]
)
import time
t0 = time.time()
calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config, period=period, nprocs_to_use=5)
print("Execution time: {} s".format(time.time() - t0))
#calculate_lake_effect_snowfall(label_to_config=label_to_config, period=period)
label = "CRCM5_NEMO"
vname_map = {}
vname_map.update(vname_map_CRCM5)
vname_map.update({
default_varname_mappings.SNOWFALL_RATE: "U3"
})
label_to_config_CRCM5 = OrderedDict([(
label, {
"base_folder": "/HOME/huziy/skynet3_rech1/CRCM5_outputs/coupled-GL-NEMO1h/selected_fields",
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"min_dt": timedelta(hours=3),
"varname_mapping": vname_map,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
)])
# calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config_CRCM5,
# period=period,
# nprocs_to_use=16)
label = "CRCM5_Hostetler"
vname_map = {}
vname_map.update(vname_map_CRCM5)
vname_map.update({
default_varname_mappings.SNOWFALL_RATE: "U3"
})
label_to_config_CRCM5 = OrderedDict([(
label, {
"base_folder": "/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"min_dt": timedelta(hours=3),
"varname_mapping": vname_map,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"filename_prefix_mapping": vname_to_fname_prefix_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
)])
def monthly_func(x):
print(x)
return calculate_lake_effect_snowfall_each_year_in_parallel(**x)
def main():
period = Period(
datetime(1979, 12, 1), datetime(1988, 3, 1)
)
# should be consequent
months_of_interest = [12, 1, 2]
period.months_of_interest = months_of_interest
vname_to_level_erai = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
ERAI_label = "ERA-Interim"
label = ERAI_label
label_to_config = OrderedDict(
[ # ERA-Interim
(label,
{
"base_folder": "/RECH/data/Driving_data/Offline/ERA-Interim_0.75/6h_Analysis",
"data_source_type": data_source_types.ALL_VARS_IN_A_FOLDER_OF_RPN_FILES,
"min_dt": timedelta(hours=6),
"varname_mapping": vname_map_CRCM5,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
),
# Add additional sources below
]
)
# calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config, period=period)
# calculate_lake_effect_snowfall(label_to_config=label_to_config, period=period)
label = "CRCM5_NEMO"
label_to_config_CRCM5 = OrderedDict([(
label, {
"base_folder": "/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/Samples",
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT,
"min_dt": timedelta(hours=3),
"varname_mapping": vname_map_CRCM5,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"filename_prefix_mapping": vname_to_fname_prefix_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
)])
# calculate_lake_effect_snowfall(label_to_config=label_to_config_CRCM5, period=period)
# calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config_CRCM5, period=period)
label = "CRCM5_Hostetler"
label_to_config_CRCM5 = OrderedDict([(
label, {
"base_folder": "/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"min_dt": timedelta(hours=3),
"varname_mapping": vname_map_CRCM5,
"level_mapping": vname_to_level_erai,
"offset_mapping": vname_to_offset_CRCM5,
"multiplier_mapping": vname_to_multiplier_CRCM5,
"filename_prefix_mapping": vname_to_fname_prefix_CRCM5,
"out_folder": "lake_effect_analysis_{}_{}-{}".format(label, period.start.year, period.end.year)
}
)])
calculate_lake_effect_snowfall_each_year_in_parallel(label_to_config=label_to_config_CRCM5, period=period)