Example: convert WRF-LES output to DEPHY format

Example: convert WRF-LES output to DEPHY format#

Code to read WRF-LES output files and write to DEPHY format (NetCDF)

Contributed by Tim Juliano from NCAR, last updated on 12/1/23

Import libraries#

import xarray as xr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import csv
import os
import netCDF4
import datetime as dt
from netCDF4 import Dataset
import glob
import copy
from scipy import interpolate

Specify directory locations#

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

Processed LES/SCM outputs are invited for commit to the GitHub repository on a user-specified branch under /comble-mip/output_XXX/YOUR_MODEL_NAME/sandbox/YOUR_RUN_NAME where output_XXX can be either output_les or output_scm. These can be committed and removed at any time. A reminder that file naming conventions may be found here.

# specify source for forcing file
myforcingfile = '/glade/scratch/tjuliano/doe_comble/intercomparison/dephy_forcing/COMBLE_INTERCOMPARISON_FORCING_V2.3.nc'

# specify source directory for model outputs
my_rootdir = '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/'

################################ CHANGE SIM NAME HERE
my_simname = 'Lx25km_dx100m_new_z0_v2.3_tim'
#my_simname = 'Lx25km_dx100m_def_z0'
#my_simname = 'Lx25km_dx100m_new_z0_noice'
################################

################################ CHANGE DEPHY FILE NAME HERE
dephy_filename = 'WRF_Lx25_dx100_FixN.nc'
#dephy_filename = 'WRF_Lx25_dx100_FixN_def_z0.nc'
#dephy_filename = 'WRF_Lx25_dx100_FixN_noice.nc'
################################

my_rundir = my_rootdir + my_simname
are_there_subdirs = False
if are_there_subdirs:
    subdir_name = 'run'
    my_subdirs = sorted(glob.glob(my_rundir + '/'+subdir_name+'*/'))

# specify save directory
my_savedir = './output_les/wrf/sandbox/'
if not os.path.exists(my_savedir):
    os.makedirs(my_savedir)
    
# minutes between WRF samples
delt_min = 10.
delt = delt_min*60

# consider ice fields?
do_ice = True
if 'noice' in dephy_filename:
    do_ice = False
    
# consider prognostic aerosol fields?
do_progNa = False
if 'ProgNa' in dephy_filename:
    do_progNa = True

Read forcing file and get model height at mid points and edges#

We will need this info to interpolate WRF outputs from native levels to requested levels, as model levels drift slightly during the simulation due to WRF’s sigma vertical coordinate system#

forcing_params = xr.open_dataset(myforcingfile,decode_times=False)
zw_grid = forcing_params['zw_grid'].data # model layer top points
zm_grid = (zw_grid[1:] + zw_grid[0:-1]) / 2. # model layer mid points

Read WRF data#

Read parameters settings#

if are_there_subdirs:
    wrfout_filenames = sorted(glob.glob(my_subdirs[0] + 'wrfout*'))
else:
    wrfout_filenames = sorted(glob.glob(my_rundir + '/wrfout*'))
wrfout_params = xr.open_dataset(wrfout_filenames[0],decode_times=False)
wrf_dx = wrfout_params.DX
wrf_dy = wrfout_params.DY
wrf_nx = wrfout_params.dims['west_east']
wrf_ny = wrfout_params.dims['south_north']
wrf_nz = wrfout_params.dims['bottom_top']
wrf_lat = wrfout_params['XLAT'].data.mean()

Gather all files#

if are_there_subdirs:
    for i in np.arange(len(my_subdirs)):
        if i == 0:
            input_filenames = sorted(glob.glob(my_subdirs[i] + 'wrfstat*'))
        else:
            input_filenames = np.concatenate((input_filenames,sorted(glob.glob(my_subdirs[i] + 'wrfstat*'))))
else:
    input_filenames = sorted(glob.glob(my_rundir + '/wrfstat*'))
print (input_filenames)

['/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_22:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_23:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_00:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_01:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_02:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_03:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_04:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_05:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_06:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_07:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_08:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_09:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_10:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_11:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_12:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_13:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_14:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_15:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_16:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_17:00:00', '/glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_18:00:00']

Subset variables depending on 1D time series, domain mean soundings, etc.#

wrf_file = xr.open_dataset(input_filenames[0],decode_times=False)
    
# find all WRF variables
wrf_vars = [i for i in wrf_file.data_vars]
#print (wrf_vars)

# find subset of variables for 1D time series (these variables start with 'CST')
wrf_vars_cst = [j for j in wrf_vars if ('CST' in j or 'Times' in j)]

# find subset of variables for 2D (time x height) soundings (these variables start with 'CSP')
wrf_vars_csp = [j for j in wrf_vars if ('CSP' in j or 'Times' in j)]

Read domain-mean profiles#

skip_t0_next_file = False
for i in np.arange(len(input_filenames)):
    print ('Doing ', input_filenames[i])
    dummy_file = xr.open_dataset(input_filenames[i],decode_times=False)
    if i == 0:
        wrf_snds = dummy_file[wrf_vars_csp]
    else:
        if skip_t0_next_file:
            wrf_snds = xr.concat([wrf_snds,dummy_file[wrf_vars_csp].isel(Time=np.arange(1,12))],dim="Time")
            skip_t0_next_file = False
        else:
            wrf_snds = xr.concat([wrf_snds,dummy_file[wrf_vars_csp]],dim="Time")
            
    if len(dummy_file['Times']) == 1:
        skip_t0_next_file = True

Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_22:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_23:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_00:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_01:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_02:00:00
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Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_04:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_05:00:00
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Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_12:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_13:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_14:00:00
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Read domain-mean scalars#

skip_t0_next_file = False
for i in np.arange(len(input_filenames)):
    print ('Doing ', input_filenames[i])
    dummy_file = xr.open_dataset(input_filenames[i],decode_times=False)
    if i == 0:
        wrf_scas = dummy_file[wrf_vars_cst]
    else:
        if skip_t0_next_file:
            wrf_scas = xr.concat([wrf_scas,dummy_file[wrf_vars_cst].isel(Time=np.arange(1,12))],dim="Time")
            skip_t0_next_file = False
        else:
            wrf_scas = xr.concat([wrf_scas,dummy_file[wrf_vars_cst]],dim="Time")        
        
    if len(dummy_file['Times']) == 1:
        skip_t0_next_file = True

Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_22:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-12_23:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_00:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_01:00:00
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Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_06:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_07:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_08:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_09:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_10:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_11:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_12:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_13:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_14:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_15:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_16:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_17:00:00
Doing  /glade/scratch/tjuliano/doe_comble/wrf_asr_cao/WRF-ASR-CAO/WRF/test/GOOD_SIMS_1DOM/2023_pan_gass/v2.2/Lx25km_dx100m_new_z0_v2.3_tim/wrfstat_d01_2020-03-13_18:00:00

Unstagger any staggered variables and add to dataset#

# vertical velocity
csp_w_mass = (wrf_snds['CSP_W'][:,0:-1].data+wrf_snds['CSP_W'][:,1:].data)/2.
wrf_snds = wrf_snds.assign(variables={'CSP_W_MASS': (('Time', 'bottom_top'), csp_w_mass)})

# vertical velocity variance
csp_w2_mass = (wrf_snds['CSP_W2'][:,0:-1].data+wrf_snds['CSP_W2'][:,1:].data)/2.
wrf_snds = wrf_snds.assign(variables={'CSP_W2_MASS': (('Time', 'bottom_top'), csp_w2_mass)})

Calculate some additional variables requested, and add them to the xarray#

# obukhov length
cst_mol = 1./wrf_scas['CST_RMOL'].data
wrf_scas = wrf_scas.assign(variables={'CST_MOL': (('Time'), cst_mol)})

# total optical depth
cst_opd = wrf_scas['CST_OPDC'].data+wrf_scas['CST_OPDR'].data+wrf_scas['CST_OPDI'].data+wrf_scas['CST_OPDS'].data+wrf_scas['CST_OPDG'].data
wrf_scas = wrf_scas.assign(variables={'CST_OPD': (('Time'), cst_opd)})

# liquid water path
cst_lwp = wrf_scas['CST_CLWP'].data+wrf_scas['CST_RWP'].data
wrf_scas = wrf_scas.assign(variables={'CST_LWP': (('Time'), cst_lwp)})

# frozen water path
cst_fwp = wrf_scas['CST_IWP'].data+wrf_scas['CST_SWP'].data+wrf_scas['CST_GWP'].data
wrf_scas = wrf_scas.assign(variables={'CST_FWP': (('Time'), cst_fwp)})

# air temperature
Rd = 287.
Cp = 1004.5
csp_t = pow(wrf_snds['CSP_P'].data/100000.,Rd/Cp)*wrf_snds['CSP_TH'].data
wrf_snds = wrf_snds.assign(variables={'CSP_T': (('Time', 'bottom_top'), csp_t)})

# filter QNC to zero where QC=0.
csp_qnc_hold = wrf_snds['CSP_QNC'].data
csp_qc_hold = wrf_snds['CSP_QC'].data
csp_qnc_filter = copy.deepcopy(csp_qnc_hold)
for i in np.arange(np.shape(csp_qc_hold)[0]):
    idxx = np.where(csp_qc_hold[i,:]==0.)[0]
    csp_qnc_filter[i,idxx] = 0.
wrf_snds = wrf_snds.assign(variables={'CSP_QNC_FILTER': (('Time', 'bottom_top'), csp_qnc_filter)})

# filter CSP_THDT_COND to zero where QT=0.
csp_thdt_cond_hold = wrf_snds['CSP_THDT_COND'].data
csp_qt_hold = wrf_snds['CSP_QL'].data+wrf_snds['CSP_QF'].data
csp_thdt_cond_filter = copy.deepcopy(csp_thdt_cond_hold)
for i in np.arange(np.shape(csp_qt_hold)[0]):
    idxx = np.where(csp_qt_hold[i,:]==0.)[0]
    csp_thdt_cond_filter[i,idxx] = 0.
wrf_snds = wrf_snds.assign(variables={'CSP_THDT_COND_FILTER': (('Time', 'bottom_top'), csp_thdt_cond_filter)})

/glade/derecho/scratch/tjuliano/tmp/ipykernel_53145/3722589477.py:2: RuntimeWarning: divide by zero encountered in divide
  cst_mol = 1./wrf_scas['CST_RMOL'].data

Interpolate profile variables from WRF’s native vertical grid to requested vertical grid#

Inefficient right now, but only takes ~15 seconds…#

csp_z_new = copy.deepcopy(wrf_snds['CSP_Z'])
csp_z_new[:,:] = zm_grid
csp_zw_new = copy.deepcopy(wrf_snds['CSP_Z8W'])
csp_zw_new[:,:] = zw_grid
for t in np.arange(1,np.shape(csp_zw_new)[0]): # loop over time
    for i in wrf_snds.data_vars:
        if 'CSP' in i:
            if 'CSP_Z' not in i:
                if 'CSP_DZ' not in i:
                    if 'bottom_top' in wrf_snds[i].dims:
                        f = interpolate.interp1d(wrf_snds['CSP_Z'][t,:].data, wrf_snds[i][t,:].data, kind='linear', fill_value="extrapolate")
                        wrf_snds[i][t,:] = f(csp_z_new[t,:])
                    elif 'bottom_top_stag' in wrf_snds[i].dims:
                        f = interpolate.interp1d(wrf_snds['CSP_Z8W'][t,:].data, wrf_snds[i][t,:].data, kind='linear', fill_value="extrapolate")
                        wrf_snds[i][t,:] = f(csp_zw_new[t,:])

Prepare output file in DEPHY format#

Read requested variables list#

Variable description, naming, units, and dimensions.

# read list of requested variables
vars_mean_list = pd.read_excel('https://docs.google.com/spreadsheets/d/1Vl8jYGviet7EtXZuQiitrx4NSkV1x27aJAhxxjBb9zI/export?gid=0&format=xlsx',
                              sheet_name='Mean')

pd.set_option('display.max_rows', None)
vars_mean_list

	standard_name	variable_id	units	dimensions	comment (10-min average reported at endpoints, green=minimum)
0	time	time	s	–	dimension, seconds since 2020-03-12 18:00:00
1	height	zf	m	–	dimension, altitude of mid-level points above ...
2	layer_top_height	ze	m	–	dimension, altitude of layer top points above ...
3	surface_pressure	ps	Pa	time	–
4	surface_temperature	ts	K	time	–
5	surface_friction_velocity	ustar	m s-1	time	–
6	surface_roughness_length_for_momentum_in_air	z0	m	time	–
7	surface_roughness_length_for_heat_in_air	z0h	m	time	–
8	surface_roughness_length_for_humidity_in_air	z0q	m	time	–
9	surface_upward_sensible_heat_flux	hfss	W m-2	time	–
10	surface_upward_latent_heat_flux	hfls	W m-2	time	–
11	obukhov_length	ol	m	time	–
12	atmosphere_mass_content_of_liquid_cloud_water	lwpc	kg m-2	time	default breakdown to cloud water and rain in a...
13	atmosphere_mass_content_of_rain_water	lwpr	kg m-2	time	–
14	atmosphere_mass_content_of_ice_water	iwp	kg m-2	time	all ice-phase hydrometeors
15	cloud_area_fraction	clt	1	time	fraction of atmospheric columns with total hyd...
16	optical_depth	od	1	time	scene (all sky); mid-visible, all hydrometeors
17	optical_depth_of_liquid_cloud	odlc	1	time	as for optical_depth but cloud droplets only
18	precipitation_flux_at_surface	pr	kg m-2 s-1	time	liquid and ice phase, all hydrometeors
19	precipitation_flux_at_surface_in_ice_phase	pri	kg m-2 s-1	time	ice phase hydrometeors only
20	toa_outgoing_longwave_flux	rlut	W m-2	time	at top of atmosphere
21	surface_downwelling_longwave_flux	rlds	W m-2	time	–
22	surface_upwelling_longwave_flux	rlus	W m-2	time	–
23	surface_sea_spray_number_flux	ssaf	m-2 s-1	time	when using prognostic aerosol; emission only (...
24	air_pressure	pa	Pa	time, height	–
25	eastward_wind	ua	m s-1	time, height	–
26	northward_wind	va	m s-1	time, height	–
27	air_dry_density	rhoa	kg m-3	time, height	–
28	air_temperature	ta	K	time, height	–
29	water_vapor_mixing_ratio	qv	kg kg-1	time, height	per kg dry air
30	relative_humidity	hur	1	time, height	relative to liquid
31	relative_humidity_over_ice	huri	1	time, height	relative to ice
32	air_potential_temperature	theta	K	time, height	–
33	specific_turbulent_kinetic_energy_resolved	tke_res	m2 s-2	time, height	resolved only
34	specific_turbulent_kinetic_energy_sgs	tke_sgs	m2 s-2	time, height	SGS only
35	mass_mixing_ratio_of_cloud_liquid_water_in_air	qlc	kg kg-1	time, height	scene (all sky) per kg dry air; default breakd...
36	mass_mixing_ratio_of_rain_water_in_air	qlr	kg kg-1	time, height	–
37	mass_mixing_ratio_of_cloud_ice_in_air	qic	kg kg-1	time, height	default breakdown to cloud_ice, snow and graup...
38	mass_mixing_ratio_of_snow_in_air	qis	kg kg-1	time, height	–
39	mass_mixing_ratio_of_graupel_in_air	qig	kg kg-1	time, height	–
40	number_of_liquid_cloud_droplets_in_air	nlc	kg-1	time, height	scene (all sky) per kg dry air; default breakd...
41	number_of_rain_drops_in_air	nlr	kg-1	time, height	–
42	number_of_cloud_ice_crystals_in_air	nic	kg-1	time, height	default breakdown to cloud_ice, snow and graup...
43	number_of_snow_crystals_in_air	nis	kg-1	time, height	–
44	number_of_graupel_crystals_in_air	nig	kg-1	time, height	–
45	number_of_total_aerosol_mode1	na1	kg-1	time, height	when using prognostic aerosol; scene (all sky)...
46	number_of_total_aerosol_mode2	na2	kg-1	time, height	accumulation mode
47	number_of_total_aerosol_mode3	na3	kg-1	time, height	sea spray mode
48	number_of_liquid_cloud_droplets_in_cloud	nlcic	kg-1	time, height	per kg dry air where cloud water > 0.01 g/kg; ...
49	number_of_ice_crystals_in_cloud	niic	kg-1	time, height	total ice hydrometeors per kg dry air where cl...
50	disspation_rate_of_turbulent_kinetic_energy	eps	m2 s-3	time, height	total (including numerical diffusion contribut...
51	zonal_momentum_flux	uw	m2 s-2	time, height	total (resolved plus SGS)
52	meridional_momentum_flux	vw	m2 s-2	time, height	total (resolved plus SGS)
53	variance_of_upward_air_velocity	w2	m2 s-2	time, height	total (resolved plus SGS)
54	vertical_flux_potential_temperature	wth	K m s-1	time, height	total (resolved plus SGS)
55	vertical_flux_liquid_ice_water_potential_tempe...	vf_thli	K m s-1	time, height	total (resolved plus SGS); include sedimentati...
56	vertical_flux_water_vapor	wqv	kg kg-1 m s-1	time, height	total (resolved plus SGS)
57	vertical_flux_total_water	vf_qt	kg kg-1 m s-1	time, height	total (resolved plus SGS); vapor+all liquid+al...
58	area_fraction_of_liquid_cloud	flc	1	time, height	fraction of cells with cloud water threshold o...
59	precipitation_flux_in_air	prf	kg m-2 s-1	time, height	liquid and ice phase, all hydrometeors
60	precipitation_flux_in_air_in_ice_phase	prfi	kg m-2 s-1	time, height	–
61	downwelling_longwave_flux_in_air	rld	W m-2	time, height	–
62	upwelling_longwave_flux_in_air	rlu	W m-2	time, height	–
63	tendency_of_air_potential_temperature_due_to_r...	dth_rad	K s-1	time, height	–
64	tendency_of_air_potential_temperature_due_to_m...	dth_micro	K s-1	time, height	including net condensation and precipitation
65	tendency_of_air_potential_temperature_due_to_m...	dth_turb	K s-1	time, height	including surface fluxes
66	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_micro	kg kg-1 s-1	time, height	including net condensation and precipitation
67	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_turb	kg kg-1 s-1	time, height	including surface fluxes
68	tendency_of_aerosol_number_due_to_warm_microph...	dna_micro_warm	kg-1 s-1	time, height	activated and unactivated aerosol (sum over al...
69	tendency_of_aerosol_number_due_to_cold_microph...	dna_micro_cold	kg-1 s-1	time, height	activated and unactivated aerosol (sum over al...
70	tendency_of_aerosol_number_due_to_mixing	dna_turb	kg-1 s-1	time, height	activated and unactivated aerosol (sum over al...
71	tendency_of_ice_number_due_to_heterogeneous_fr...	dni_het	kg-1 s-1	time, height	sum of primary ice nucleation due to activatio...
72	tendency_of_ice_number_due_to_secondary_ice_pr...	dni_sip	kg-1 s-1	time, height	sum of secondary ice formation processes (e.g....
73	tendency_of_ice_number_due_to_homogeneous_free...	dni_hom	kg-1 s-1	time, height	ice nucleation source due to homogoeneous free...

Match WRF variables to requested outputs#

Expand the table to include columns that indicate WRF model variable names and any conversion factor.

# drop comments
vars_mean_list = vars_mean_list.drop(vars_mean_list.filter(regex='comment').columns, axis=1)

# add columns to contain model output name and units conversion factors
vars_mean_list = vars_mean_list.assign(model_name='missing data',conv_factor=1.0)

# identify requested variables with only time dimension
vars_mean_scas = vars_mean_list[vars_mean_list['dimensions']=='time']

# match to WRF variable names and specify conversion factors
for index in vars_mean_scas.index:
    standard_name = vars_mean_list.standard_name.iat[index]
    if standard_name=='surface_pressure': 
        vars_mean_list.model_name.iat[index] = 'CST_PS'
    if standard_name=='surface_temperature': 
        vars_mean_list.model_name.iat[index] = 'CST_TSK'
    if standard_name=='surface_friction_velocity': 
        vars_mean_list.model_name.iat[index] = 'CST_UST'
    if standard_name=='surface_roughness_length_for_momentum_in_air': 
        vars_mean_list.model_name.iat[index] = 'CST_ZNT'
    if standard_name=='surface_roughness_length_for_heat_in_air': 
        vars_mean_list.model_name.iat[index] = 'CST_Z0T'
    if standard_name=='surface_roughness_length_for_humidity_in_air': 
        vars_mean_list.model_name.iat[index] = 'CST_Z0Q'
    if standard_name=='surface_upward_sensible_heat_flux': 
        vars_mean_list.model_name.iat[index] = 'CST_SH'
    if standard_name=='surface_upward_latent_heat_flux': 
        vars_mean_list.model_name.iat[index] = 'CST_LH'
    if standard_name=='obukhov_length': 
        vars_mean_list.model_name.iat[index] = 'CST_MOL'
    if standard_name=='atmosphere_mass_content_of_liquid_cloud_water': 
        vars_mean_list.model_name.iat[index] = 'CST_CLWP'
    if standard_name=='atmosphere_mass_content_of_rain_water': 
        vars_mean_list.model_name.iat[index] = 'CST_RWP'
    if standard_name=='atmosphere_mass_content_of_ice_water': 
        vars_mean_list.model_name.iat[index] = 'CST_FWP'
    if standard_name=='cloud_area_fraction': 
        vars_mean_list.model_name.iat[index] = 'CST_CLDTOT2'
    if standard_name=='optical_depth': 
        vars_mean_list.model_name.iat[index] = 'CST_OPD'
    if standard_name=='optical_depth_of_liquid_cloud': 
        vars_mean_list.model_name.iat[index] = 'CST_OPDC'
    if standard_name=='precipitation_flux_at_surface':
        vars_mean_list.model_name.iat[index] = 'CST_PRECT'
    if do_ice:
        if standard_name=='precipitation_flux_at_surface_in_ice_phase': 
            vars_mean_list.model_name.iat[index] = 'CST_PRECI'
    if standard_name=='toa_outgoing_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'CST_FLNT'
    if standard_name=='surface_downwelling_longwave_flux':
        vars_mean_list.model_name.iat[index] = 'CST_RLDS'
    if standard_name=='surface_upwelling_longwave_flux':
        vars_mean_list.model_name.iat[index] = 'CST_FLNS'

# identify requested variables with time and vertical dimensions
vars_mean_snds = vars_mean_list[vars_mean_list['dimensions']=='time, height']

for index in vars_mean_snds.index:
    standard_name = vars_mean_list.standard_name.iat[index]
    if standard_name=='air_pressure': 
        vars_mean_list.model_name.iat[index] = 'CSP_P'
    if standard_name=='eastward_wind': 
        vars_mean_list.model_name.iat[index] = 'CSP_U'
    if standard_name=='northward_wind': 
        vars_mean_list.model_name.iat[index] = 'CSP_V'
    if standard_name=='air_dry_density': 
        vars_mean_list.model_name.iat[index] = 'CSP_RHO'
    if standard_name=='air_temperature': 
        vars_mean_list.model_name.iat[index] = 'CSP_T'
    if standard_name=='water_vapor_mixing_ratio': 
        vars_mean_list.model_name.iat[index] = 'CSP_QV'
    if standard_name=='relative_humidity': 
        vars_mean_list.model_name.iat[index] = 'CSP_RH'
        vars_mean_list.conv_factor.iat[index] = 1/100.
    if standard_name=='relative_humidity_over_ice': 
        vars_mean_list.model_name.iat[index] = 'CSP_RHI'
        vars_mean_list.conv_factor.iat[index] = 1/100.
    if standard_name=='air_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'CSP_TH'
    if standard_name=='specific_turbulent_kinetic_energy_resolved': 
        vars_mean_list.model_name.iat[index] = 'CSP_TKE_RS'
    if standard_name=='specific_turbulent_kinetic_energy_sgs': 
        vars_mean_list.model_name.iat[index] = 'CSP_TKE_SGS'
    if standard_name=='mass_mixing_ratio_of_cloud_liquid_water_in_air': 
        vars_mean_list.model_name.iat[index] = 'CSP_QC'
    if standard_name=='mass_mixing_ratio_of_rain_water_in_air': 
        vars_mean_list.model_name.iat[index] = 'CSP_QR'
    if do_ice:
        if standard_name=='mass_mixing_ratio_of_cloud_ice_in_air': 
            vars_mean_list.model_name.iat[index] = 'CSP_QI'
        if standard_name=='mass_mixing_ratio_of_snow_in_air': 
            vars_mean_list.model_name.iat[index] = 'CSP_QS'
        if standard_name=='mass_mixing_ratio_of_graupel_in_air':
            vars_mean_list.model_name.iat[index] = 'CSP_QG'
    if standard_name=='number_of_liquid_cloud_droplets_in_air':
        vars_mean_list.model_name.iat[index] = 'CSP_QNC_FILTER'
    if standard_name=='number_of_rain_drops_in_air':
        vars_mean_list.model_name.iat[index] = 'CSP_QNR'
    if do_ice:
        if standard_name=='number_of_cloud_ice_crystals_in_air':
            vars_mean_list.model_name.iat[index] = 'CSP_QNI'
        if standard_name=='number_of_snow_crystals_in_air':
            vars_mean_list.model_name.iat[index] = 'CSP_QNS'
        if standard_name=='number_of_graupel_crystals_in_air':
            vars_mean_list.model_name.iat[index] = 'CSP_QNG'
    if do_progNa:
        if standard_name=='number_of_total_aerosol_mode1':
            vars_mean_list.model_name.iat[index] = 'na_1'
        if standard_name=='number_of_total_aerosol_mode2':
            vars_mean_list.model_name.iat[index] = 'na_2'
        if standard_name=='number_of_total_aerosol_mode3':
            vars_mean_list.model_name.iat[index] = 'na_3'     
    
    if standard_name=='number_of_liquid_cloud_droplets_in_cloud': 
        vars_mean_list.model_name.iat[index] = 'CSP_QNC_C'
    if standard_name=='number_of_ice_crystals_in_cloud': 
        vars_mean_list.model_name.iat[index] = 'CSP_QNI_C'
    if standard_name=='disspation_rate_of_turbulent_kinetic_energy': 
        vars_mean_list.model_name.iat[index] = 'CSP_TKE_DI'
    
    if standard_name=='zonal_momentum_flux': 
        vars_mean_list.model_name.iat[index] = 'CSP_UW'
    if standard_name=='meridional_momentum_flux': 
        vars_mean_list.model_name.iat[index] = 'CSP_VW'
    if standard_name=='variance_of_upward_air_velocity': 
        vars_mean_list.model_name.iat[index] = 'CSP_W2_MASS'
    if standard_name=='vertical_flux_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'CSP_WTH'
    if standard_name=='vertical_flux_liquid_ice_water_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'CSP_WTHLI'
    if standard_name=='vertical_flux_water_vapor': 
        vars_mean_list.model_name.iat[index] = 'CSP_WQV'
    if standard_name=='vertical_flux_total_water': 
        vars_mean_list.model_name.iat[index] = 'CSP_WQT'
    if standard_name=='area_fraction_of_liquid_cloud': 
        vars_mean_list.model_name.iat[index] = 'CSP_A_CL'
    if standard_name=='precipitation_flux_in_air': 
        vars_mean_list.model_name.iat[index] = 'CSP_SEDFQT'
    if standard_name=='precipitation_flux_in_air_in_ice_phase': 
        vars_mean_list.model_name.iat[index] = 'CSP_SEDFQF'
    #if standard_name=='downwelling_longwave_flux_in_air': 
    #    vars_mean_list.model_name.iat[index] = 'LWdn'
    #if standard_name=='upwelling_longwave_flux_in_air': 
    #    vars_mean_list.model_name.iat[index] = 'LWup'
    if standard_name=='tendency_of_air_potential_temperature_due_to_radiative_heating': 
        vars_mean_list.model_name.iat[index] = 'CSP_THDT_LW'
    if standard_name=='tendency_of_air_potential_temperature_due_to_microphysics': 
        vars_mean_list.model_name.iat[index] = 'CSP_THDT_COND_FILTER'
    if standard_name=='tendency_of_air_potential_temperature_due_to_mixing': 
        vars_mean_list.model_name.iat[index] = 'CSP_THDT_TURB'
    if standard_name=='tendency_of_water_vapor_mixing_ratio_due_to_microphysics': 
        vars_mean_list.model_name.iat[index] = 'CSP_QVDT_COND'
    if standard_name=='tendency_of_water_vapor_mixing_ratio_due_to_mixing': 
        vars_mean_list.model_name.iat[index] = 'CSP_QVDT_TURB'

Sanity check#

vars_mean_list[3:] # echo variables (first three rows are dimensions)

	standard_name	variable_id	units	dimensions	model_name	conv_factor
3	surface_pressure	ps	Pa	time	CST_PS	1.00
4	surface_temperature	ts	K	time	CST_TSK	1.00
5	surface_friction_velocity	ustar	m s-1	time	CST_UST	1.00
6	surface_roughness_length_for_momentum_in_air	z0	m	time	CST_ZNT	1.00
7	surface_roughness_length_for_heat_in_air	z0h	m	time	CST_Z0T	1.00
8	surface_roughness_length_for_humidity_in_air	z0q	m	time	CST_Z0Q	1.00
9	surface_upward_sensible_heat_flux	hfss	W m-2	time	CST_SH	1.00
10	surface_upward_latent_heat_flux	hfls	W m-2	time	CST_LH	1.00
11	obukhov_length	ol	m	time	CST_MOL	1.00
12	atmosphere_mass_content_of_liquid_cloud_water	lwpc	kg m-2	time	CST_CLWP	1.00
13	atmosphere_mass_content_of_rain_water	lwpr	kg m-2	time	CST_RWP	1.00
14	atmosphere_mass_content_of_ice_water	iwp	kg m-2	time	CST_FWP	1.00
15	cloud_area_fraction	clt	1	time	CST_CLDTOT2	1.00
16	optical_depth	od	1	time	CST_OPD	1.00
17	optical_depth_of_liquid_cloud	odlc	1	time	CST_OPDC	1.00
18	precipitation_flux_at_surface	pr	kg m-2 s-1	time	CST_PRECT	1.00
19	precipitation_flux_at_surface_in_ice_phase	pri	kg m-2 s-1	time	CST_PRECI	1.00
20	toa_outgoing_longwave_flux	rlut	W m-2	time	CST_FLNT	1.00
21	surface_downwelling_longwave_flux	rlds	W m-2	time	CST_RLDS	1.00
22	surface_upwelling_longwave_flux	rlus	W m-2	time	CST_FLNS	1.00
23	surface_sea_spray_number_flux	ssaf	m-2 s-1	time	missing data	1.00
24	air_pressure	pa	Pa	time, height	CSP_P	1.00
25	eastward_wind	ua	m s-1	time, height	CSP_U	1.00
26	northward_wind	va	m s-1	time, height	CSP_V	1.00
27	air_dry_density	rhoa	kg m-3	time, height	CSP_RHO	1.00
28	air_temperature	ta	K	time, height	CSP_T	1.00
29	water_vapor_mixing_ratio	qv	kg kg-1	time, height	CSP_QV	1.00
30	relative_humidity	hur	1	time, height	CSP_RH	0.01
31	relative_humidity_over_ice	huri	1	time, height	CSP_RHI	0.01
32	air_potential_temperature	theta	K	time, height	CSP_TH	1.00
33	specific_turbulent_kinetic_energy_resolved	tke_res	m2 s-2	time, height	CSP_TKE_RS	1.00
34	specific_turbulent_kinetic_energy_sgs	tke_sgs	m2 s-2	time, height	CSP_TKE_SGS	1.00
35	mass_mixing_ratio_of_cloud_liquid_water_in_air	qlc	kg kg-1	time, height	CSP_QC	1.00
36	mass_mixing_ratio_of_rain_water_in_air	qlr	kg kg-1	time, height	CSP_QR	1.00
37	mass_mixing_ratio_of_cloud_ice_in_air	qic	kg kg-1	time, height	CSP_QI	1.00
38	mass_mixing_ratio_of_snow_in_air	qis	kg kg-1	time, height	CSP_QS	1.00
39	mass_mixing_ratio_of_graupel_in_air	qig	kg kg-1	time, height	CSP_QG	1.00
40	number_of_liquid_cloud_droplets_in_air	nlc	kg-1	time, height	CSP_QNC_FILTER	1.00
41	number_of_rain_drops_in_air	nlr	kg-1	time, height	CSP_QNR	1.00
42	number_of_cloud_ice_crystals_in_air	nic	kg-1	time, height	CSP_QNI	1.00
43	number_of_snow_crystals_in_air	nis	kg-1	time, height	CSP_QNS	1.00
44	number_of_graupel_crystals_in_air	nig	kg-1	time, height	CSP_QNG	1.00
45	number_of_total_aerosol_mode1	na1	kg-1	time, height	missing data	1.00
46	number_of_total_aerosol_mode2	na2	kg-1	time, height	missing data	1.00
47	number_of_total_aerosol_mode3	na3	kg-1	time, height	missing data	1.00
48	number_of_liquid_cloud_droplets_in_cloud	nlcic	kg-1	time, height	CSP_QNC_C	1.00
49	number_of_ice_crystals_in_cloud	niic	kg-1	time, height	CSP_QNI_C	1.00
50	disspation_rate_of_turbulent_kinetic_energy	eps	m2 s-3	time, height	CSP_TKE_DI	1.00
51	zonal_momentum_flux	uw	m2 s-2	time, height	CSP_UW	1.00
52	meridional_momentum_flux	vw	m2 s-2	time, height	CSP_VW	1.00
53	variance_of_upward_air_velocity	w2	m2 s-2	time, height	CSP_W2_MASS	1.00
54	vertical_flux_potential_temperature	wth	K m s-1	time, height	CSP_WTH	1.00
55	vertical_flux_liquid_ice_water_potential_tempe...	vf_thli	K m s-1	time, height	CSP_WTHLI	1.00
56	vertical_flux_water_vapor	wqv	kg kg-1 m s-1	time, height	CSP_WQV	1.00
57	vertical_flux_total_water	vf_qt	kg kg-1 m s-1	time, height	CSP_WQT	1.00
58	area_fraction_of_liquid_cloud	flc	1	time, height	CSP_A_CL	1.00
59	precipitation_flux_in_air	prf	kg m-2 s-1	time, height	CSP_SEDFQT	1.00
60	precipitation_flux_in_air_in_ice_phase	prfi	kg m-2 s-1	time, height	CSP_SEDFQF	1.00
61	downwelling_longwave_flux_in_air	rld	W m-2	time, height	missing data	1.00
62	upwelling_longwave_flux_in_air	rlu	W m-2	time, height	missing data	1.00
63	tendency_of_air_potential_temperature_due_to_r...	dth_rad	K s-1	time, height	CSP_THDT_LW	1.00
64	tendency_of_air_potential_temperature_due_to_m...	dth_micro	K s-1	time, height	CSP_THDT_COND_FILTER	1.00
65	tendency_of_air_potential_temperature_due_to_m...	dth_turb	K s-1	time, height	CSP_THDT_TURB	1.00
66	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_micro	kg kg-1 s-1	time, height	CSP_QVDT_COND	1.00
67	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_turb	kg kg-1 s-1	time, height	CSP_QVDT_TURB	1.00
68	tendency_of_aerosol_number_due_to_warm_microph...	dna_micro_warm	kg-1 s-1	time, height	missing data	1.00
69	tendency_of_aerosol_number_due_to_cold_microph...	dna_micro_cold	kg-1 s-1	time, height	missing data	1.00
70	tendency_of_aerosol_number_due_to_mixing	dna_turb	kg-1 s-1	time, height	missing data	1.00
71	tendency_of_ice_number_due_to_heterogeneous_fr...	dni_het	kg-1 s-1	time, height	missing data	1.00
72	tendency_of_ice_number_due_to_secondary_ice_pr...	dni_sip	kg-1 s-1	time, height	missing data	1.00
73	tendency_of_ice_number_due_to_homogeneous_free...	dni_hom	kg-1 s-1	time, height	missing data	1.00