dataset['masked_land_transport']=(('months_ax','lat','lon'), masked_land_transport)
if __name__ == "__main__":
import nc_file_io_xarray as io
import set_and_get_params as sagp
input_dir='/scratch/rg419/GFDL_model/GFDLmoistModel/'
base_dir='/scratch/rg419/Data_moist/'
land_file='input/land.nc'
base_exp_name='amip_10m/'
exp_name='amip_10m/'
start_file=121
end_file=480
land_present=True
topo_present=False
avg_or_daily='daily'
model_params = sagp.model_params_set(input_dir, delta_t=720., ml_depth=10.)
dataset, time_arr, size_list = io.read_data( base_dir,exp_name,start_file,end_file,avg_or_daily,topo_present)
land_array, topo_array = io.read_land(input_dir,base_exp_name,land_present,True,size_list,land_file)
dataset['land'] = (('lat','lon'),land_array)
qflux_calc(dataset, model_params)
land_present = True
use_interpolated_pressure_level_data = False #Conditions the script on whether to expect data on sigma levels (if False) or pressure levels (if True). Script should be insensitive to this choice if both sets of files exist.
#Set time increments of input files (e.g. `monthly` for `atmos_monthly` files.
avg_or_daily = 'monthly'
#Set the time frequency of output data. Valid options are 'months', 'all_time' or 'dayofyear'.
time_divisions_of_qflux_to_be_calculated = 'months'
model_params = sagp.model_params_set(input_dir,
delta_t=720.,
ml_depth=20.,
res=42)
dataset, time_arr, size_list = io.read_data(
base_dir, exp_name, start_file, end_file, avg_or_daily,
use_interpolated_pressure_level_data)
land_array, topo_array = io.read_land(
input_dir, base_exp_name, land_present,
use_interpolated_pressure_level_data, size_list, land_file)
dataset['land'] = (('lat', 'lon'), land_array)
check_surface_flux_dims(dataset)
qflux_calc(dataset,
model_params,
output_file_name,
ice_file_name,
groupby_name=time_divisions_of_qflux_to_be_calculated)