Example: convert DHARMA LES output to DEPHY format

Example: convert DHARMA LES output to DEPHY format#

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

Contributed by Ann Fridlind from NASA/GISS

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

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 domain-mean outputs are invited for commit to the GitHub repository on a user-specified branch under /comble-mip/output_les/YOUR_MODEL_NAME/sandbox/YOUR_RUN_NAME. These can be committed and removed at any time.

It is requested to name your baseline small-domain run directory as ‘Lx25km_dx100m’ (in place of YOUR_RUN_NAME), so that it can readily be automatically compared with other runs using the same test specification.

# specify start time of simulation and simulation name
start_dtime = '2020-03-12 22:00:00.0'

# specify input data directory name, traceable to source machine, and
# specify output file name (see file naming convention in TOC)
#my_readdir = 'case0313_diagn_ice25_miz_cfmip_dx100'
#my_outfile = 'DHARMA_Lx25km_dx100_FixN.nc'

#my_readdir = 'case0313_progn_ice25_miz_cfmip_dx100'
#my_outfile = 'DHARMA_Lx25km_dx100_ProgNa.nc'

my_readdir = 'case0313_diag_ice25_nomiz_dx200_specZ0_nudge'
my_outfile = 'DHARMA_Lx25_dx200_FixN_spec_z0_nudged.nc'

# specify local source directories (additional subdirectories if restart was required)
#my_rundir = '/data/home/fridlind/dharma/sandbox/' + my_readdir + '/'
my_rundir = '/data/home/floriantornow/dharma/sandbox/' + my_readdir + '/'
my_outdirs = sorted([f for f in os.listdir(my_rundir) if not f.startswith('.')], key=str.lower)
print(my_outdirs)

# specify Github scratch directory where processed model output will be committed
my_gitdir = '../../output_les/dharma/sandbox/'

['0-20h']

Read DHARMA data#

Read domain-mean profiles#

Read domain-mean scalars#

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	height	altitude of layer top points above sea surface...
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	air_pressure	pa	Pa	time, height	–
24	eastward_wind	ua	m s-1	time, height	–
25	northward_wind	va	m s-1	time, height	–
26	air_dry_density	rhoa	kg m-3	time, height	–
27	air_temperature	ta	K	time, height	–
28	water_vapor_mixing_ratio	qv	kg kg-1	time, height	per kg dry air
29	relative_humidity	hur	1	time, height	relative to liquid
30	relative_humidity_over_ice	huri	1	time, height	relative to ice
31	air_potential_temperature	theta	K	time, height	–
32	specific_turbulent_kinetic_energy_resolved	tke_res	m2 s-2	time, height	resolved only
33	specific_turbulent_kinetic_energy_sgs	tke_sgs	m2 s-2	time, height	SGS only
34	mass_mixing_ratio_of_cloud_liquid_water_in_air	qlc	kg kg-1	time, height	scene (all sky) per kg dry air; default breakd...
35	mass_mixing_ratio_of_rain_water_in_air	qlr	kg kg-1	time, height	–
36	mass_mixing_ratio_of_cloud_ice_in_air	qic	kg kg-1	time, height	default breakdown to cloud_ice, snow and graup...
37	mass_mixing_ratio_of_snow_in_air	qis	kg kg-1	time, height	–
38	mass_mixing_ratio_of_graupel_in_air	qig	kg kg-1	time, height	–
39	number_of_liquid_cloud_droplets_in_air	nlc	kg-1	time, height	scene (all sky) per kg dry air; default breakd...
40	number_of_rain_drops_in_air	nlr	kg-1	time, height	–
41	number_of_cloud_ice_crystals_in_air	nic	kg-1	time, height	default breakdown to cloud_ice, snow and graup...
42	number_of_snow_crystals_in_air	nis	kg-1	time, height	–
43	number_of_graupel_crystals_in_air	nig	kg-1	time, height	–
44	number_of_total_aerosol_mode1	na1	kg-1	time, height	when using prognostic aerosol; scene (all sky)...
45	number_of_total_aerosol_mode2	na2	kg-1	time, height	accumulation mode
46	number_of_total_aerosol_mode3	na3	kg-1	time, height	sea spray mode
47	number_of_liquid_cloud_droplets_in_cloud	nlcic	kg-1	time, height	per kg dry air where cloud water > 0.01 g/kg; ...
48	number_of_ice_crystals_in_cloud	niic	kg-1	time, height	total ice hydrometeors per kg dry air where cl...
49	disspation_rate_of_turbulent_kinetic_energy	eps	m2 s-3	time, height	total (including numerical diffusion contribut...
50	zonal_momentum_flux	uw	m2 s-2	time, height	total (resolved plus SGS)
51	meridional_momentum_flux	vw	m2 s-2	time, height	total (resolved plus SGS)
52	variance_of_upward_air_velocity	w2	m2 s-2	time, height	total (resolved plus SGS)
53	vertical_flux_potential_temperature	wth	K m s-1	time, height	total (resolved plus SGS)
54	vertical_flux_liquid_ice_water_potential_tempe...	vf_thli	K m s-1	time, height	total (resolved plus SGS); include sedimentati...
55	vertical_flux_water_vapor	wqv	kg kg-1 m s-1	time, height	total (resolved plus SGS)
56	vertical_flux_total_water	vf_qt	kg kg-1 m s-1	time, height	total (resolved plus SGS); vapor+all liquid+al...
57	area_fraction_of_liquid_cloud	flc	1	time, height	fraction of cells with cloud water threshold o...
58	precipitation_flux_in_air	prf	kg m-2 s-1	time, height	liquid and ice phase, all hydrometeors
59	precipitation_flux_in_air_in_ice_phase	prfi	kg m-2 s-1	time, height	–
60	downwelling_longwave_flux_in_air	rld	W m-2	time, height	–
61	upwelling_longwave_flux_in_air	rlu	W m-2	time, height	–
62	tendency_of_air_potential_temperature_due_to_r...	dth_rad	K s-1	time, height	–
63	tendency_of_air_potential_temperature_due_to_m...	dth_micro	K s-1	time, height	including net condensation and precipitation
64	tendency_of_air_potential_temperature_due_to_m...	dth_turb	K s-1	time, height	including surface fluxes
65	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_micro	kg kg-1 s-1	time, height	including net condensation and precipitation
66	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_turb	kg kg-1 s-1	time, height	including surface fluxes

Match DHARMA variables to requested outputs#

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

# drop comments
vars_mean_list = vars_mean_list.drop(columns='comment (10-min average reported at endpoints, green=minimum)')

# 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 DHARMA 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] = 'Psurf'
    if standard_name=='surface_temperature': 
        vars_mean_list.model_name.iat[index] = 'avg_T_sfc'
    if standard_name=='surface_friction_velocity': 
        vars_mean_list.model_name.iat[index] = 'avg_ustar'
    if standard_name=='surface_upward_sensible_heat_flux': 
        vars_mean_list.model_name.iat[index] = 'avg_T_flx'
    if standard_name=='surface_upward_latent_heat_flux': 
        vars_mean_list.model_name.iat[index] = 'avg_qv_flx'
    if standard_name=='obukhov_length': 
        vars_mean_list.model_name.iat[index] = 'avg_obk'
    if standard_name=='atmosphere_mass_content_of_liquid_cloud_water': 
        vars_mean_list.model_name.iat[index] = 'cwp'
        vars_mean_list.conv_factor.iat[index] = 1/1000.
    if standard_name=='atmosphere_mass_content_of_rain_water': 
        vars_mean_list.model_name.iat[index] = 'rwp'
        vars_mean_list.conv_factor.iat[index] = 1/1000.
    if do_ice:
        if standard_name=='atmosphere_mass_content_of_ice_water': 
            vars_mean_list.model_name.iat[index] = 'iwp'
            vars_mean_list.conv_factor.iat[index] = 1/1000.
    if standard_name=='cloud_area_fraction': 
        vars_mean_list.model_name.iat[index] = 'colf_opd'
    if standard_name=='optical_depth': 
        vars_mean_list.model_name.iat[index] = 'opd_tot'
    if standard_name=='optical_depth_of_liquid_cloud': 
        vars_mean_list.model_name.iat[index] = 'opd_cloud'
    if standard_name=='precipitation_flux_at_surface': 
        vars_mean_list.model_name.iat[index] = 'avg_precip'
        vars_mean_list.conv_factor.iat[index] = 1/3600.
    if do_ice:
        if standard_name=='precipitation_flux_at_surface_in_ice_phase': 
            vars_mean_list.model_name.iat[index] = 'avg_precip_ice'
            vars_mean_list.conv_factor.iat[index] = 1/3600.
    if standard_name=='optical_depth_of_cloud_droplets': 
        vars_mean_list.model_name.iat[index] = 'opd_cloud'
    if standard_name=='toa_outgoing_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'LWupTOA'
    if standard_name=='surface_downwelling_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'LWdnSFC'  
    if standard_name=='surface_upwelling_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'LWupSFC'  

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

# match to DHARMA variable names and specify conversion factors
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] = 'pressure'
    if standard_name=='eastward_wind': 
        vars_mean_list.model_name.iat[index] = 'u'
    if standard_name=='northward_wind': 
        vars_mean_list.model_name.iat[index] = 'v'
    if standard_name=='air_dry_density': 
        vars_mean_list.model_name.iat[index] = 'rhobar'
    if standard_name=='air_temperature': 
        vars_mean_list.model_name.iat[index] = 'T'
    if standard_name=='water_vapor_mixing_ratio': 
        vars_mean_list.model_name.iat[index] = 'qv'
    if standard_name=='relative_humidity': 
        vars_mean_list.model_name.iat[index] = 'RH'
        vars_mean_list.conv_factor.iat[index] = 1/100.
    if standard_name=='relative_humidity_over_ice': 
        vars_mean_list.model_name.iat[index] = 'RHI'
        vars_mean_list.conv_factor.iat[index] = 1/100.
    if standard_name=='air_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'theta'
    if standard_name=='specific_turbulent_kinetic_energy_resolved': 
        vars_mean_list.model_name.iat[index] = 'tkeavg'
    if standard_name=='specific_turbulent_kinetic_energy_sgs': 
        vars_mean_list.model_name.iat[index] = 'tke_smag'
    if standard_name=='mass_mixing_ratio_of_cloud_liquid_water_in_air': 
        vars_mean_list.model_name.iat[index] = 'qc'
    if standard_name=='mass_mixing_ratio_of_rain_water_in_air': 
        vars_mean_list.model_name.iat[index] = 'qr'
    if do_ice:
        if standard_name=='mass_mixing_ratio_of_cloud_ice_in_air': 
            vars_mean_list.model_name.iat[index] = 'qic'
        if standard_name=='mass_mixing_ratio_of_snow_in_air': 
            vars_mean_list.model_name.iat[index] = 'qif'
        if standard_name=='mass_mixing_ratio_of_graupel_in_air': 
            vars_mean_list.model_name.iat[index] = 'qid'
    if standard_name=='number_of_liquid_cloud_droplets_in_air': 
        vars_mean_list.model_name.iat[index] = 'nc'
    if standard_name=='number_of_rain_drops_in_air': 
        vars_mean_list.model_name.iat[index] = 'nr'
    if do_ice:
        if standard_name=='number_of_cloud_ice_crystals_in_air': 
            vars_mean_list.model_name.iat[index] = 'nic'
        if standard_name=='number_of_snow_crystals_in_air': 
            vars_mean_list.model_name.iat[index] = 'nif'
        if standard_name=='number_of_graupel_crystals_in_air': 
            vars_mean_list.model_name.iat[index] = 'nid'    
    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=='disspation_rate_of_turbulent_kinetic_energy': 
#        vars_mean_list.model_name.iat[index] = ''
    if standard_name=='zonal_momentum_flux': 
        vars_mean_list.model_name.iat[index] = 'uw_zt'
    if standard_name=='meridional_momentum_flux': 
        vars_mean_list.model_name.iat[index] = 'vw_zt'
    if standard_name=='variance_of_upward_air_velocity': 
        vars_mean_list.model_name.iat[index] = 'w2_zt'
    if standard_name=='vertical_flux_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'wth_zt'
    if standard_name=='vertical_flux_liquid_ice_water_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'wthli_zt'
    if standard_name=='vertical_flux_water_vapor': 
        vars_mean_list.model_name.iat[index] = 'wqv_zt'
    if standard_name=='vertical_flux_total_water': 
        vars_mean_list.model_name.iat[index] = 'wqt_zt'
    if standard_name=='area_fraction_of_liquid_cloud': 
        vars_mean_list.model_name.iat[index] = 'cloud_f'
    if standard_name=='precipitation_flux_in_air': 
        vars_mean_list.model_name.iat[index] = 'PF'
        vars_mean_list.conv_factor.iat[index] = 1/(3600.)
    if standard_name=='precipitation_flux_in_air_in_ice_phase': 
        vars_mean_list.model_name.iat[index] = 'PFi'
        vars_mean_list.conv_factor.iat[index] = 1/(3600.)
    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] = 'Srad'
        vars_mean_list.conv_factor.iat[index] = 1/3600.
    if standard_name=='tendency_of_air_potential_temperature_due_to_microphysics': 
        vars_mean_list.model_name.iat[index] = 'dth_micro'
        vars_mean_list.conv_factor.iat[index] = 1/3600.
    if standard_name=='tendency_of_air_potential_temperature_due_to_mixing': 
        vars_mean_list.model_name.iat[index] = 'Sth_adv'
        vars_mean_list.conv_factor.iat[index] = 1/3600.
    if standard_name=='tendency_of_water_vapor_mixing_ratio_due_to_microphysics': 
        vars_mean_list.model_name.iat[index] = 'dq_micro'
        vars_mean_list.conv_factor.iat[index] = 1/3600.*1/1000.
    if standard_name=='tendency_of_water_vapor_mixing_ratio_due_to_mixing': 
        vars_mean_list.model_name.iat[index] = 'Sqv_adv'

vars_mean_list[2:] # echo variables (first two rows are dimensions)

	standard_name	variable_id	units	dimensions	model_name	conv_factor
2	layer_top_height	ze	m	height	missing data	1.000000e+00
3	surface_pressure	ps	Pa	time	Psurf	1.000000e+00
4	surface_temperature	ts	K	time	avg_T_sfc	1.000000e+00
5	surface_friction_velocity	ustar	m s-1	time	avg_ustar	1.000000e+00
6	surface_roughness_length_for_momentum_in_air	z0	m	time	missing data	1.000000e+00
7	surface_roughness_length_for_heat_in_air	z0h	m	time	missing data	1.000000e+00
8	surface_roughness_length_for_humidity_in_air	z0q	m	time	missing data	1.000000e+00
9	surface_upward_sensible_heat_flux	hfss	W m-2	time	avg_T_flx	1.000000e+00
10	surface_upward_latent_heat_flux	hfls	W m-2	time	avg_qv_flx	1.000000e+00
11	obukhov_length	ol	m	time	avg_obk	1.000000e+00
12	atmosphere_mass_content_of_liquid_cloud_water	lwpc	kg m-2	time	cwp	1.000000e-03
13	atmosphere_mass_content_of_rain_water	lwpr	kg m-2	time	rwp	1.000000e-03
14	atmosphere_mass_content_of_ice_water	iwp	kg m-2	time	iwp	1.000000e-03
15	cloud_area_fraction	clt	1	time	colf_opd	1.000000e+00
16	optical_depth	od	1	time	opd_tot	1.000000e+00
17	optical_depth_of_liquid_cloud	odlc	1	time	opd_cloud	1.000000e+00
18	precipitation_flux_at_surface	pr	kg m-2 s-1	time	avg_precip	2.777778e-04
19	precipitation_flux_at_surface_in_ice_phase	pri	kg m-2 s-1	time	avg_precip_ice	2.777778e-04
20	toa_outgoing_longwave_flux	rlut	W m-2	time	LWupTOA	1.000000e+00
21	surface_downwelling_longwave_flux	rlds	W m-2	time	LWdnSFC	1.000000e+00
22	surface_upwelling_longwave_flux	rlus	W m-2	time	LWupSFC	1.000000e+00
23	air_pressure	pa	Pa	time, height	pressure	1.000000e+00
24	eastward_wind	ua	m s-1	time, height	u	1.000000e+00
25	northward_wind	va	m s-1	time, height	v	1.000000e+00
26	air_dry_density	rhoa	kg m-3	time, height	rhobar	1.000000e+00
27	air_temperature	ta	K	time, height	T	1.000000e+00
28	water_vapor_mixing_ratio	qv	kg kg-1	time, height	qv	1.000000e+00
29	relative_humidity	hur	1	time, height	RH	1.000000e-02
30	relative_humidity_over_ice	huri	1	time, height	RHI	1.000000e-02
31	air_potential_temperature	theta	K	time, height	theta	1.000000e+00
32	specific_turbulent_kinetic_energy_resolved	tke_res	m2 s-2	time, height	tkeavg	1.000000e+00
33	specific_turbulent_kinetic_energy_sgs	tke_sgs	m2 s-2	time, height	tke_smag	1.000000e+00
34	mass_mixing_ratio_of_cloud_liquid_water_in_air	qlc	kg kg-1	time, height	qc	1.000000e+00
35	mass_mixing_ratio_of_rain_water_in_air	qlr	kg kg-1	time, height	qr	1.000000e+00
36	mass_mixing_ratio_of_cloud_ice_in_air	qic	kg kg-1	time, height	qic	1.000000e+00
37	mass_mixing_ratio_of_snow_in_air	qis	kg kg-1	time, height	qif	1.000000e+00
38	mass_mixing_ratio_of_graupel_in_air	qig	kg kg-1	time, height	qid	1.000000e+00
39	number_of_liquid_cloud_droplets_in_air	nlc	kg-1	time, height	nc	1.000000e+00
40	number_of_rain_drops_in_air	nlr	kg-1	time, height	nr	1.000000e+00
41	number_of_cloud_ice_crystals_in_air	nic	kg-1	time, height	nic	1.000000e+00
42	number_of_snow_crystals_in_air	nis	kg-1	time, height	nif	1.000000e+00
43	number_of_graupel_crystals_in_air	nig	kg-1	time, height	nid	1.000000e+00
44	number_of_total_aerosol_mode1	na1	kg-1	time, height	missing data	1.000000e+00
45	number_of_total_aerosol_mode2	na2	kg-1	time, height	missing data	1.000000e+00
46	number_of_total_aerosol_mode3	na3	kg-1	time, height	missing data	1.000000e+00
47	number_of_liquid_cloud_droplets_in_cloud	nlcic	kg-1	time, height	missing data	1.000000e+00
48	number_of_ice_crystals_in_cloud	niic	kg-1	time, height	missing data	1.000000e+00
49	disspation_rate_of_turbulent_kinetic_energy	eps	m2 s-3	time, height	missing data	1.000000e+00
50	zonal_momentum_flux	uw	m2 s-2	time, height	uw_zt	1.000000e+00
51	meridional_momentum_flux	vw	m2 s-2	time, height	vw_zt	1.000000e+00
52	variance_of_upward_air_velocity	w2	m2 s-2	time, height	w2_zt	1.000000e+00
53	vertical_flux_potential_temperature	wth	K m s-1	time, height	wth_zt	1.000000e+00
54	vertical_flux_liquid_ice_water_potential_tempe...	vf_thli	K m s-1	time, height	wthli_zt	1.000000e+00
55	vertical_flux_water_vapor	wqv	kg kg-1 m s-1	time, height	wqv_zt	1.000000e+00
56	vertical_flux_total_water	vf_qt	kg kg-1 m s-1	time, height	wqt_zt	1.000000e+00
57	area_fraction_of_liquid_cloud	flc	1	time, height	cloud_f	1.000000e+00
58	precipitation_flux_in_air	prf	kg m-2 s-1	time, height	PF	2.777778e-04
59	precipitation_flux_in_air_in_ice_phase	prfi	kg m-2 s-1	time, height	PFi	2.777778e-04
60	downwelling_longwave_flux_in_air	rld	W m-2	time, height	LWdn	1.000000e+00
61	upwelling_longwave_flux_in_air	rlu	W m-2	time, height	LWup	1.000000e+00
62	tendency_of_air_potential_temperature_due_to_r...	dth_rad	K s-1	time, height	Srad	2.777778e-04
63	tendency_of_air_potential_temperature_due_to_m...	dth_micro	K s-1	time, height	dth_micro	2.777778e-04
64	tendency_of_air_potential_temperature_due_to_m...	dth_turb	K s-1	time, height	Sth_adv	2.777778e-04
65	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_micro	kg kg-1 s-1	time, height	dq_micro	2.777778e-07
66	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_turb	kg kg-1 s-1	time, height	Sqv_adv	1.000000e+00

Example: convert DHARMA LES output to DEPHY format

Contents

Example: convert DHARMA LES output to DEPHY format#

Import libraries#

Specify directory locations#

Read DHARMA data#

Read set-up parameters#

Read domain-mean profiles#

Profile unit conversions and sundry#

Read domain-mean scalars#

Prepare output file in DEPHY format#

Read requested variables list#

Match DHARMA variables to requested outputs#

Create DEPHY output file#

Check output file#