Example: convert DEPHY forcing to DHARMA LES and ModelE3 SCM formats

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Example: convert DEPHY forcing to DHARMA LES and ModelE3 SCM formats#

This code reads the COMBLE-MIP LES/SCM forcing file that is supplied in the DEPHY format and writes input files that are formatted for the DHARMA LES (ASCII) and the ModelE3 ESM SCM (NetCDF)

Contributed by Ann Fridlind from NASA/GISS on 1/24/23

Import libraries#

import numpy as np
import pandas as pd
import xarray as xr
import netCDF4
from netCDF4 import Dataset

Specify local write directory#

If on the ARM JupyterHub, it is recommended to create and specify a local directory that is outside of the COMBLE-MIP repository to write input files in your model’s format

If on the ARM JupyterHub, these files can be conveniently downloaded to your local machine and will remain when you log out

my_write_dir = '../../../my_input_files/'

Specify wind forcing type#

do_geo = True

Create input files for ModelE3 SCM#

Write initialization and forcing files in NetCDF format

Write initialization file#

# open file with all components at initial time only
filename_init_SCM = my_write_dir + 'SCM_COMBLE-MIP_init.nc'
scm_init = Dataset('./' + filename_init_SCM,mode='w',format='NETCDF3_CLASSIC')

# create dimensions
lev_dim = scm_init.createDimension('lev', n_z) # level axis is pressure for SCM
time_dim = scm_init.createDimension('time', 1) # initial time only

# create variables
lev = scm_init.createVariable('lev', np.float64, ('lev',))
lev.long_name = 'air pressure'
lev.units = 'mb'
lev[:] = dephy['pressure'].values/100.

Ps = scm_init.createVariable('Ps', np.float64, ('time',))
Ps.long_name = 'surface pressure'
Ps.units = 'mb'
Ps[:] = dephy.ps.values.round(1)/100.

T = scm_init.createVariable('T', np.float64, ('lev',))
T.long_name = 'air temperature'
T.units = 'K'
T[:] = dephy['temp'].values

Q = scm_init.createVariable('Q', np.float64, ('lev',))
Q.long_name = 'water vapor mixing ratio'
Q.units = 'kg kg-1'
Q[:] = dephy['qv'].values

# create time arrays
year = scm_init.createVariable('year', np.int16, ('time',))
year[:] = pd.to_datetime(dephy['time'].values[0]).year
month = scm_init.createVariable('month', np.int16, ('time',))
month[:] = pd.to_datetime(dephy['time'].values[0]).month
day = scm_init.createVariable('day', np.int16, ('time',))
day[:] = pd.to_datetime(dephy['time'].values[0]).day
hour = scm_init.createVariable('hour', np.int16, ('time',))
hour[:] = pd.to_datetime(dephy['time'].values[0]).hour

# close
scm_init.close()

Write forcing file#

# open forcing file with all components on same time dimension
filename_forcing_SCM = my_write_dir + 'SCM_COMBLE-MIP_force.nc'
scm_force = Dataset('./' + filename_forcing_SCM,mode='w',format='NETCDF3_CLASSIC')

# create dimensions
lev_dim = scm_force.createDimension('lev', n_z) # level axis is pressure for SCM
time_dim = scm_force.createDimension('time', n_t)

# create variables
lev = scm_force.createVariable('lev', np.float64, ('lev'))
lev.long_name = 'air pressure'
lev.units = 'mb'
lev[:] = dephy['pressure'].values/100.

ts = scm_force.createVariable('Tskin', np.float32, ('time'))
ts.long_name = 'surface temperature'
ts.units = 'K'
ts[:] = dephy['ts'].values

ug = scm_force.createVariable('Ug', np.float64, ('time','lev',))
ug.units = 'm s-1'
ug.long_name = 'geostrophic zonal wind'
ug[:] = dephy['ug'].values

vg = scm_force.createVariable('Vg', np.float64, ('time','lev',))
vg.units = 'm s-1'
vg.long_name = 'geostrophic meridional wind'
vg[:] = dephy['vg'].values

# create time arrays
year = scm_force.createVariable('year', np.int16, ('time',))
year[:] = pd.to_datetime(dephy['time'].values).year
month = scm_force.createVariable('month', np.int16, ('time',))
month[:] = pd.to_datetime(dephy['time'].values).month
day = scm_force.createVariable('day', np.int16, ('time',))
day[:] = pd.to_datetime(dephy['time'].values).day
hour = scm_force.createVariable('hour', np.int16, ('time',))
hour[:] = pd.to_datetime(dephy['time'].values).hour

# close
scm_force.close()