Example: convert E3SMv3 SCM output to DEPHY format#
Code to read E3SMv3 output files and write to DEPHY format (NetCDF)
Contributed by Ann Fridlind from NASA/GISS, Meng Zhang from LLNL
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 scipy.stats
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 domain-mean outputs are invited for commit to the GitHub repository on a user-specified branch under /comble-mip/output_scm/YOUR_MODEL_NAME/sandbox/YOUR_OUTPUT_FILE_NAME. These can be committed and removed at any time.
If you are able to make a run without ice, it is requested to append ‘noice’ to YOUR_OUTPUT_FILE_NAME, so that it can readily be automatically compared with the baseline and other liquid-only runs.
# specify input and output file names: versions of ModelE3 with and without ice
# Phys
# my_input_suffix = '.eam.h0.2020-03-12-79200.nc'
# my_output_suffix = 'Phys_FixN_def_z0_alt_no_ugvg.nc'
# Phys without ice
my_input_suffix = '.eam.h0.2020-03-12-79200.nc'
my_output_suffix = 'Phys_FixN_noice_def_z0_alt_no_ugvg.nc'
# Tun1
# my_input_suffix = 't698ml.nc'
# my_output_suffix = 'Tun1_FixN.nc'
# Tun1 without ice
# my_input_suffix = 't698ml_noice.nc'
# my_output_suffix = 'Tun1_FixN_noice.nc'
# Tun2
# my_input_suffix = 't705ml.nc'
# my_output_suffix = 'Tun2_FixN.nc'
# Tun2 without ice
# my_input_suffix = 't705ml_noice.nc'
# my_output_suffix = 'Tun2_FixN_noice.nc'
# specify local source directories (with subdirectories for spin up over ice and restart over water)
my_input_dir = '/home/mzhang1/Data_test/COMBLE-MIP-SCM/'
# specify Github scratch directory where processed model output will be committed (automate later)
my_output_filename = 'E3SMv3dev-' + my_output_suffix
my_gitdir = '/home/mzhang1/Data_test/COMBLE-MIP-SCM/gitdir/' #'../../output_scm/modelE/sandbox/'
Read E3SM data#
Read single file containing all output data#
Note: ERROR 1: PROJ… message can be ignored here.
input_filename = my_input_dir + 'COMBLE-MIP_v2_4_e3smv3_0_FixN_noice' + my_input_suffix
model_data = xr.open_dataset(input_filename)
# check if the run contains ice variables
do_ice = bool(max(model_data['TGCLDIWP'].values)>0.)
print('do_ice = ',do_ice)
# full parameter list
model_data
do_ice = False
<xarray.Dataset>
Dimensions: (ncol: 1, lev: 80, ilev: 81, P3_input_dim: 16,
P3_output_dim: 32, time: 41, nbnd: 2)
Coordinates:
* lev (lev) float64 0.1236 0.1819 0.2674 ... 993.8 998.5
* ilev (ilev) float64 0.1 0.1472 0.2166 ... 997.0 1e+03
* P3_input_dim (P3_input_dim) int32 1 2 3 4 5 ... 12 13 14 15 16
* P3_output_dim (P3_output_dim) int32 1 2 3 4 5 ... 28 29 30 31 32
* time (time) datetime64[ns] 2020-03-12T22:00:00 ... 2...
Dimensions without coordinates: ncol, nbnd
Data variables: (12/362)
lat (ncol) float64 ...
lon (ncol) float64 ...
area (ncol) float64 ...
hyam (lev) float64 ...
hybm (lev) float64 ...
P0 float64 ...
... ...
vap_ice_exchange (time, lev, ncol) float32 ...
vap_liq_exchange (time, lev, ncol) float32 ...
vap_liq_exchange_clubb (time, lev, ncol) float32 ...
wat_a1 (time, lev, ncol) float32 ...
wat_a2 (time, lev, ncol) float32 ...
wat_a3 (time, lev, ncol) float32 ...
Attributes: (12/19)
ne: 4
np: 4
title: EAM History file information
source: E3SM Atmosphere Model
source_id: 3f054ae96a
product: model-output
... ...
institution_id: E3SM-Project
institution: LLNL (Lawrence Livermore National Laboratory, Livermor...
contact: e3sm-data-support@llnl.gov
initial_file: /global/cfs/cdirs/e3sm/inputdata/atm/cam/inic/homme/ea...
topography_file: /global/cfs/cdirs/e3sm/inputdata/atm/cam/topo/USGS-gto...
time_period_freq: minute_30# 2D variables
icol = 0
# assign pressure layer
dum_var = model_data['hyam']*0.
model_data = model_data.assign(layer = dum_var + model_data['lev'].data)
# calculate air pressure
dum_var = model_data['T'][:,:,icol]*0.
dum_pa = model_data['hyam'].data * model_data['P0'].data + model_data['hybm'].data * model_data['PS'].data
model_data = model_data.assign(pa = dum_var + dum_pa)
# calculate layer top pressure
dum_var = model_data['T'][:,:,icol]*0.
dum_pe = model_data['hyai'].data * model_data['P0'].data + model_data['hybi'].data * model_data['PS'].data
model_data = model_data.assign(pe = dum_var + dum_pe[:,:-1])
# assign surface pressure
dum_var = model_data['PS'][:,icol]*0.
model_data = model_data.assign(ps = dum_var + model_data['PS'][:,icol].data)
# assign surface friction velocity
dum_var = model_data['USTAR'][:,icol]*0.
model_data = model_data.assign(ustar = dum_var + model_data['USTAR'][:,icol].data)
# assign surface temperature
dum_var = model_data['TS'][:,icol]*0.
model_data = model_data.assign(ts = dum_var + model_data['TS'][:,icol].data)
# assign surface upward sensible heat flux
dum_var = model_data['SHFLX'][:,icol]*0.
model_data = model_data.assign(hfss = dum_var + model_data['SHFLX'][:,icol].data)
# assign surface upward latent heat flux
dum_var = model_data['LHFLX'][:,icol]*0.
model_data = model_data.assign(hfls = dum_var + model_data['LHFLX'][:,icol].data)
# assign obukhov length
dum_var = model_data['OBKLEN'][:,icol]*0.
model_data = model_data.assign(ol = dum_var + model_data['OBKLEN'][:,icol].data)
# assign PBL height
dum_var = model_data['PBLH'][:,icol]*0.
model_data = model_data.assign(pblh = dum_var + model_data['PBLH'][:,icol].data)
# calculate liquid water path
dum_var = model_data['TGCLDLWP'][:,icol]*0.
pint = model_data['hyai'].data * model_data['P0'].data + model_data['hybi'].data * model_data['PS'].data
pdel = pint[:,1:]-np.roll(pint, 1, axis=1)[:,1:]
dum_lwpc = (model_data['CLDLIQ'][:,:,0].data * pdel / 9.80665).sum(axis=1)
model_data = model_data.assign(lwpc = dum_var + dum_lwpc)
# calculate rain water path
dum_var = model_data['TGCLDLWP'][:,icol]*0.
dum_lwpr = (model_data['RAINQM'][:,:,0].data * pdel / 9.80665).sum(axis=1)
model_data = model_data.assign(lwpr = dum_var + dum_lwpr)
# calculate ice water path
dum_var = model_data['TGCLDLWP'][:,icol]*0.
dum_iwp = (model_data['CLDICE'][:,:,0].data * pdel / 9.80665).sum(axis=1)
# dum_iwp = dum_iwp + (model_data['SNOWQM'][:,:,0].data * pdel / 9.80665).sum(axis=1) # NOT for P3
model_data = model_data.assign(iwp = dum_var + dum_iwp)
# assign cf
dum_var = model_data['CLDTOT'][:,icol]*0.
model_data = model_data.assign(cf = dum_var + model_data['CLDTOT'][:,icol].data)
# assign cfc
dum_var = model_data['FREQZM'][:,icol]*0.
model_data = model_data.assign(cfc = dum_var + model_data['CONCLD'][:,:,icol].max(axis=1).data)
# # assign optical depth
# dum_var = model_data['TOT_ICLD_VISTAU']*0.
# model_data = model_data.assign(od = dum_var + model_data['TOT_ICLD_VISTAU'].data)
# # assign optical depth of liquid cloud
# dum_var = model_data['LIQ_ICLD_VISTAU']*0.
# model_data = model_data.assign(odlc = dum_var + model_data['LIQ_ICLD_VISTAU'].data)
# calculate pr
dum_var = model_data['PRECL'][:,icol]*0.
model_data = model_data.assign(pr = dum_var + (model_data['PRECC'][:,icol].data + model_data['PRECL'][:,icol].data) * 1000.)
# calculate pri
dum_var = model_data['PRECL'][:,icol]*0.
model_data = model_data.assign(pri = dum_var + (model_data['PRECSC'][:,icol].data + model_data['PRECSL'][:,icol].data) * 1000.)
# calculate rlut
dum_var = model_data['FLUT'][:,icol]*0.
model_data = model_data.assign(rlut = dum_var + model_data['FLUT'][:,icol].data)
# calculate rlds
dum_var = model_data['FLDS'][:,icol]*0.
model_data = model_data.assign(rlds = dum_var + model_data['FLDS'][:,icol].data)
# calculate rlus
dum_var = model_data['FLNS'][:,icol]*0.
model_data = model_data.assign(rlus = dum_var + model_data['FLNS'][:,icol].data + model_data['FLDS'][:,icol].data)
# 3D variables
# assign qlcs
dum_var = model_data['CLDLIQ'][:,:,icol]*0.
model_data = model_data.assign(qlcs = dum_var + model_data['CLDLIQ'][:,:,icol].data)
# assign qlrs
dum_var = model_data['RAINQM'][:,:,icol]*0.
model_data = model_data.assign(qlrs = dum_var + model_data['RAINQM'][:,:,icol].data)
# assign qics
dum_var = model_data['CLDICE'][:,:,icol]*0.
model_data = model_data.assign(qics = dum_var + model_data['CLDICE'][:,:,icol].data)
# assign qips
# dum_var = model_data['SNOWQM'][:,:,icol]*0.
# model_data = model_data.assign(qips = dum_var + model_data['SNOWQM'][:,:,icol].data) # Not for P3
# assign nlcs
dum_var = model_data['NUMLIQ'][:,:,icol]*0.
model_data = model_data.assign(nlcs = dum_var + model_data['NUMLIQ'][:,:,icol].data)
# assign nlrs
dum_var = model_data['NUMRAI'][:,:,icol]*0.
model_data = model_data.assign(nlrs = dum_var + model_data['NUMRAI'][:,:,icol].data)
# assign nics
dum_var = model_data['NUMICE'][:,:,icol]*0.
model_data = model_data.assign(nics = dum_var + model_data['NUMICE'][:,:,icol].data)
# assign nips
# dum_var = model_data['NUMSNO'][:,:,icol]*0.
# model_data = model_data.assign(nips = dum_var + model_data['NUMSNO'][:,:,icol].data) # Not for P3
# assign flcs
dum_var = model_data['FREQL'][:,:,icol]*0.
model_data = model_data.assign(flcs = dum_var + model_data['FREQL'][:,:,icol].data)
# assign flrs
dum_var = model_data['FREQR'][:,:,icol]*0.
model_data = model_data.assign(flrs = dum_var + model_data['FREQR'][:,:,icol].data)
# assign fics
dum_var = model_data['FREQI'][:,:,icol]*0.
model_data = model_data.assign(fics = dum_var + model_data['FREQI'][:,:,icol].data)
# assign fips
# dum_var = model_data['FREQS'][:,:,icol]*0.
# model_data = model_data.assign(fips = dum_var + model_data['FREQS'][:,:,icol].data) # Not for P3
# assign vmlcs
# dum_var = model_data['VTRMC'][:,:,icol]*0.
# model_data = model_data.assign(vmlcs = dum_var + model_data['VTRMC'][:,:,icol].data) # Not for P3
# # assign vmlrs
# dum_var = model_data['UMR'][:,:,icol]*0.
# model_data = model_data.assign(vmlrs = dum_var + model_data['UMR'][:,:,icol].data) # Not for P3
# # assign vmics
# dum_var = model_data['VTRMI'][:,:,icol]*0.
# model_data = model_data.assign(vmics = dum_var + model_data['VTRMI'][:,:,icol].data) # Not for P3
# # assign vmips
# dum_var = model_data['UMS'][:,:,icol]*0.
# model_data = model_data.assign(vmips = dum_var + model_data['UMS'][:,:,icol].data) # Not for P3
# assign height
dum_var = model_data['Z3'][:,:,icol]*0.
model_data = model_data.assign(zf = dum_var + model_data['Z3'][:,:,icol].data)
# assign ua
dum_var = model_data['U'][:,:,icol]*0.
model_data = model_data.assign(ua = dum_var + model_data['U'][:,:,icol].data)
# assign va
dum_var = model_data['V'][:,:,icol]*0.
model_data = model_data.assign(va = dum_var + model_data['V'][:,:,icol].data)
# assign ta
dum_var = model_data['T'][:,:,icol]*0.
model_data = model_data.assign(ta = dum_var + model_data['T'][:,:,icol].data)
# assign qv
dum_var = model_data['Q'][:,:,icol]*0.
model_data = model_data.assign(qv = dum_var + model_data['Q'][:,:,icol].data)
# assign hur
dum_var = model_data['RHW'][:,:,icol]*0.
model_data = model_data.assign(hur = dum_var + model_data['RHW'][:,:,icol].data / 1e2)
# assign huri
dum_var = model_data['RHI'][:,:,icol]*0.
model_data = model_data.assign(huri = dum_var + model_data['RHI'][:,:,icol].data / 1e2)
# calculate theta
dum_var = model_data['T'][:,:,icol]*0.
pmid = model_data['hyam'].data * model_data['P0'].data + model_data['hybm'].data * model_data['PS'].data
dum_theta = model_data['T'][:,:,0].data * (1e5 / pmid) ** 0.286
model_data = model_data.assign(theta = dum_var + dum_theta)
# assign qlc
dum_var = model_data['CLDLIQ'][:,:,icol]*0.
model_data = model_data.assign(qlc = dum_var + model_data['CLDLIQ'][:,:,icol].data)
# assign qlr
dum_var = model_data['RAINQM'][:,:,icol]*0.
model_data = model_data.assign(qlr = dum_var + model_data['RAINQM'][:,:,icol].data)
# assign qi
dum_var = model_data['CLDICE'][:,:,icol]*0.
model_data = model_data.assign(qi = dum_var + model_data['CLDICE'][:,:,icol].data) # Not for P3 + model_data['SNOWQM'][:,:,icol].data)
# assign fh
dum_var = model_data['CLOUD'][:,:,icol]*0.
model_data = model_data.assign(fh = dum_var + model_data['CLOUD'][:,:,icol].data)
# assign flc
dum_var = model_data['FREQL'][:,:,icol]*0.
model_data = model_data.assign(flc = dum_var + model_data['FREQL'][:,:,icol].data)
# assign fc
dum_var = model_data['CONCLD'][:,:,icol]*0.
model_data = model_data.assign(fc = dum_var + model_data['CONCLD'][:,:,icol].data)
# calculate zonal momentum flux
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['UPWP_CLUBB'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(uw = dum_var)
# calculate meridional momentum flux
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['VPWP_CLUBB'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(vw = dum_var)
# calculate variance of upward air velocity
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['WP2_CLUBB'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(w2 = dum_var)
# calculate downwelling longwave flux in air
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['FDL'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(rld = dum_var)
# calculate downwelling longwave flux in air
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['FUL'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(rlu = dum_var)
# assign rhoa
dum_var = model_data['T'][:,:,icol]*0.
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], model_data['RHO_CLUBB'][i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(rhoa = dum_var)
# assign tke
dum_var = model_data['T'][:,:,icol]*0.
dum_tke = 0.5 * (model_data['UP2_CLUBB'].data + model_data['VP2_CLUBB'].data + model_data['WP2_CLUBB'].data)
for i in np.arange(len(dum_var[:,0])):
f_int = interpolate.interp1d(pint[i,:], dum_tke[i,:,0].data, kind='linear', fill_value='extrapolate')
dum_var[i,:] = f_int(pmid[i,:])
model_data = model_data.assign(tke = dum_var)
# calculate inversioin height
dum_var = model_data['PBLH'][:,icol]*0.
dum_dtdp = (np.roll(model_data['theta'], 1, axis=1)[:,1:] - model_data['theta'][:,1:]) / (np.roll(pmid, 1, axis=1)[:,1:] - pmid[:,1:])
dum_dtdp = np.where(pmid[:,1:] > 40000., dum_dtdp, np.nan)
indmin = np.nanargmin(dum_dtdp,axis=1)
for i in np.arange(len(indmin)):
dum_var[i] = model_data['Z3'][i,indmin[i]+1,icol].data
model_data = model_data.assign(zi = dum_var)
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=1026157027&format=xlsx',
sheet_name='SCM')
pd.set_option('display.max_rows', None)
vars_mean_list
| standard_name | variable_id | units | dimensions | comment (reported at end of each model physics time step, green=minimum, red=granularity enabling EMC2) | |
|---|---|---|---|---|---|
| 0 | time | time | s | – | dimension, seconds since 2020-03-12 18:00:00 |
| 1 | pressure_layer | layer | 1 | – | dimension, pressure layer number from 1 at sur... |
| 2 | air_pressure | pa | Pa | time, layer | pressure at mid-level points (native model lev... |
| 3 | layer_top_pressure | pe | Pa | time, layer | dimension, pressure at layer top points (used ... |
| 4 | surface_pressure | ps | Pa | time | – |
| 5 | surface_temperature | ts | K | time | – |
| 6 | surface_friction_velocity | ustar | m s-1 | time | – |
| 7 | surface_roughness_length_for_momentum_in_air | z0 | m | time | – |
| 8 | surface_roughness_length_for_heat_in_air | z0h | m | time | – |
| 9 | surface_roughness_length_for_humidity_in_air | z0q | m | time | – |
| 10 | surface_upward_sensible_heat_flux | hfss | W m-2 | time | – |
| 11 | surface_upward_latent_heat_flux | hfls | W m-2 | time | – |
| 12 | obukhov_length | ol | m | time | – |
| 13 | pbl_height | pblh | m | time | PBL scheme layer thickness (if available) |
| 14 | inversion_height | zi | m | time | sharpest vertical gradient in air_potential_te... |
| 15 | atmosphere_mass_content_of_liquid_cloud_water | lwpc | kg m-2 | time | scene (all sky); cloud water path in all class... |
| 16 | atmosphere_mass_content_of_rain_water | lwpr | kg m-2 | time | scene (all sky); rain water path in all classe... |
| 17 | atmosphere_mass_content_of_ice_water | iwp | kg m-2 | time | scene (all sky); all ice-phase hydrometeors in... |
| 18 | area_fraction_cover_of_hydrometeors | cf | 1 | time | all hydrometeors and cloud types (e.g., all ph... |
| 19 | area_fraction_cover_of_liquid_cloud | cflc | 1 | time | liquid cloud cover without precipitation, incl... |
| 20 | area_fraction_cover_of_convective_hydrometeors | cfc | 1 | time | all hydrometeors, default breakdown into conve... |
| 21 | optical_depth | od | 1 | time | scene (all sky); mid-visible, all hydrometeors... |
| 22 | optical_depth_of_liquid_cloud | odlc | 1 | time | scene (all sky); mid-visible, cloud liquid onl... |
| 23 | precipitation_flux_at_surface | pr | kg m-2 s-1 | time | scene (all sky); all hydrometeors |
| 24 | precipitation_flux_of_ice_at_surface | pri | kg m-2 s-1 | time | scene (all sky); all ice phase hydrometeors |
| 25 | toa_outgoing_longwave_flux | rlut | W m-2 | time | – |
| 26 | surface_downwelling_longwave_flux | rlds | W m-2 | time | – |
| 27 | surface_upwelling_longwave_flux | rlus | W m-2 | time | – |
| 28 | surface_sea_spray_number_flux | ssaf | m-2 s-1 | time | when using prognostic aerosol; emission only (... |
| 29 | height | zf | m | time, layer | altitude of layer mid-level points above sea s... |
| 30 | eastward_wind | ua | m s-1 | time, layer | – |
| 31 | northward_wind | va | m s-1 | time, layer | – |
| 32 | air_dry_density | rhoa | kg m-3 | time, layer | per kg dry air |
| 33 | air_temperature | ta | K | time, layer | – |
| 34 | water_vapor_mixing_ratio | qv | kg kg-1 | time, layer | – |
| 35 | relative_humidity | hur | 1 | time, layer | relative to liquid |
| 36 | relative_humidity_over_ice | huri | 1 | time, layer | relative to ice |
| 37 | air_potential_temperature | theta | K | time, layer | – |
| 38 | mass_mixing_ratio_of_cloud_liquid_water_in_air | qlc | kg kg-1 | time, layer | scene (all sky) per kg dry air; cloud water pa... |
| 39 | mass_mixing_ratio_of_rain_water_in_air | qlr | kg kg-1 | time, layer | rain water path only in all classes (e.g., con... |
| 40 | mass_mixing_ratio_of_ice_water_in_air | qi | kg kg-1 | time, layer | all ice water path in all classes (e.g., conve... |
| 41 | area_fraction_of_hydrometeors | fh | 1 | time, layer | all hydrometeors and cloud types (e.g., all ph... |
| 42 | area_fraction_of_liquid_cloud | flc | 1 | time, layer | liquid cloud cover without precipitation, incl... |
| 43 | area_fraction_of_convective_hydrometeors | fc | 1 | time, layer | all hydrometeors, default breakdown into conve... |
| 44 | precipitation_flux_in_air | prf | kg m-2 s-1 | time, layer | scene (all sky); all hydrometeors |
| 45 | precipitation_flux_in_air_in_ice_phase | prfi | kg m-2 s-1 | time, layer | scene (all sky); all ice phase hydrometeors |
| 46 | specific_turbulent_kinetic_energy | tke | m2 s-2 | time, layer | – |
| 47 | dissipation_rate_of_turbulent_kinetic_energy | eps | m2 s-3 | time, layer | report as negative |
| 48 | zonal_momentum_flux | uw | m2 s-2 | time, layer | parameterized turbulent flux |
| 49 | meridional_momentum_flux | vw | m2 s-2 | time, layer | parameterized turbulent flux |
| 50 | variance_of_upward_air_velocity | w2 | m2 s-2 | time, layer | parameterized turbulent flux |
| 51 | vertical_flux_potential_temperature | wth | K m s-1 | time, layer | parameterized turbulent flux |
| 52 | vertical_flux_liquid_ice_water_potential_tempe... | vf_thli | K m s-1 | time, layer | parameterized turbulent flux; include sediment... |
| 53 | vertical_flux_water_vapor | wqv | kg kg-1 m s-1 | time, layer | parameterized turbulent flux |
| 54 | vertical_flux_total_water | vf_qt | kg kg-1 m s-1 | time, layer | parameterized turbulent flux; vapor+all liquid... |
| 55 | convection_updraft_mass_flux | cmfu | kg m-2 s-1 | time, layer | – |
| 56 | convection_downdraft_mass_flux | cmfd | kg m-2 s-1 | time, layer | – |
| 57 | downwelling_longwave_flux_in_air | rld | W m-2 | time, layer | – |
| 58 | upwelling_longwave_flux_in_air | rlu | W m-2 | time, layer | – |
| 59 | tendency_of_air_potential_temperature_due_to_r... | dth_rad | K s-1 | time, layer | scene (all sky) |
| 60 | tendency_of_air_potential_temperature_due_to_m... | dth_micro | K s-1 | time, layer | including net condensation and precipitation i... |
| 61 | tendency_of_air_potential_temperature_due_to_m... | dth_turb | K s-1 | time, layer | including surface fluxes |
| 62 | tendency_of_water_vapor_mixing_ratio_due_to_mi... | dq_micro | s-1 | time, layer | including net condensation and precipitation i... |
| 63 | tendency_of_water_vapor_mixing_ratio_due_to_mi... | dq_turb | s-1 | time, layer | including surface fluxes |
| 64 | number_of_total_aerosol_mode1 | na1 | kg-1 | time, layer | when using prognostic aerosol; scene (all sky)... |
| 65 | number_of_total_aerosol_mode2 | na2 | kg-1 | time, layer | accumulation mode |
| 66 | number_of_total_aerosol_mode3 | na3 | kg-1 | time, layer | sea spray mode |
| 67 | tendency_of_aerosol_number_due_to_warm_microph... | dna_micro_warm | kg-1 s-1 | time, layer | activated and unactivated aerosol (sum over al... |
| 68 | tendency_of_aerosol_number_due_to_cold_microph... | dna_micro_cold | kg-1 s-1 | time, layer | activated and unactivated aerosol (sum over al... |
| 69 | tendency_of_aerosol_number_due_to_mixing | dna_turb | kg-1 s-1 | time, layer | activated and unactivated aerosol (sum over al... |
| 70 | tendency_of_ice_number_due_to_heterogeneous_fr... | dni_het | kg-1 s-1 | time, layer | sum of primary ice nucleation due to activatio... |
| 71 | tendency_of_ice_number_due_to_secondary_ice_pr... | dni_sip | kg-1 s-1 | time, layer | sum of secondary ice formation processes (e.g.... |
| 72 | tendency_of_ice_number_due_to_homogeneous_free... | dni_hom | kg-1 s-1 | time, layer | ice nucleation source due to homogoeneous free... |
| 73 | mass_mixing_ratio_of_liquid_cloud_water_in_air... | qlcs | kg kg-1 | time, layer | scene (all sky) per kg dry air; default breakd... |
| 74 | mass_mixing_ratio_of_rain_water_in_air_stratiform | qlrs | kg kg-1 | time, layer | – |
| 75 | mass_mixing_ratio_of_ice_cloud_in_air_stratiform | qics | kg kg-1 | time, layer | default breakdown as for liquid; if other ice-... |
| 76 | mass_mixing_ratio_of_ice_precipitation_in_air_... | qips | kg kg-1 | time, layer | – |
| 77 | mass_mixing_ratio_of_liquid_cloud_water_in_air... | qlcc | kg kg-1 | time, layer | – |
| 78 | mass_mixing_ratio_of_rain_water_in_air_convective | qlrc | kg kg-1 | time, layer | – |
| 79 | mass_mixing_ratio_of_ice_cloud_in_air_convective | qicc | kg kg-1 | time, layer | – |
| 80 | mass_mixing_ratio_of_ice_precipitation_in_air_... | qipc | kg kg-1 | time, layer | – |
| 81 | number_of_liquid_cloud_droplets_in_air_stratiform | nlcs | kg-1 | time, layer | scene (all sky) per kg dry air; if other categ... |
| 82 | number_of_rain_drops_in_air_stratiform | nlrs | kg-1 | time, layer | – |
| 83 | number_of_ice_cloud_crystals_in_air_stratiform | nics | kg-1 | time, layer | if other ice-phase categories are used, provid... |
| 84 | number_of_ice_precipitation_crystals_in_air_st... | nips | kg-1 | time, layer | – |
| 85 | effective_radius_of_liquid_cloud_droplets_conv... | relcc | m | time, layer | EMC2 uses effective radius for any hydrometeor... |
| 86 | effective_radius_of_rain_convective | relrc | m | time, layer | – |
| 87 | effective_radius_of_ice_cloud_convective | reicc | m | time, layer | – |
| 88 | effective_radius_of_ice_precipitation_convective | reipc | m | time, layer | – |
| 89 | area_fraction_of_liquid_cloud_stratiform | flcs | 1 | time, layer | EMC2 uses area fraction profiles for all hydro... |
| 90 | area_fraction_of_rain_stratiform | flrs | 1 | time, layer | – |
| 91 | area_fraction_of_ice_cloud_stratiform | fics | 1 | time, layer | if other ice categories are used, provide addi... |
| 92 | area_fraction_of_ice_precipitation_stratiform | fips | 1 | time, layer | – |
| 93 | area_fraction_of_liquid_cloud_convective | flcc | 1 | time, layer | – |
| 94 | area_fraction_of_rain_convective | flrc | 1 | time, layer | – |
| 95 | area_fraction_of_ice_cloud_convective | ficc | 1 | time, layer | – |
| 96 | area_fraction_of_ice_precipitation_convective | fipc | 1 | time, layer | – |
| 97 | mass_weighted_fall_speed_of_liquid_cloud_water... | vmlcs | m s-1 | time, layer | EMC2 uses mass-weighted fall-speed profiles fo... |
| 98 | mass_weighted_fall_speed_of_rain_stratiform | vmlrs | m s-1 | time, layer | – |
| 99 | mass_weighted_fall_speed_of_ice_cloud_stratiform | vmics | m s-1 | time, layer | if other ice-phase categories are used, provid... |
| 100 | mass_weighted_fall_speed_of_ice_precipitation_... | vmips | m s-1 | time, layer | – |
| 101 | mass_weighted_fall_speed_of_liquid_cloud_water... | vmlcc | m s-1 | time, layer | – |
| 102 | mass_weighted_fall_speed_of_rain_convective | vmlrc | m s-1 | time, layer | – |
| 103 | mass_weighted_fall_speed_of_cloud_ice_crystals... | vmicc | m s-1 | time, layer | – |
| 104 | mass_weighted_fall_speed_of_ice_precipitation_... | vmipc | m s-1 | time, layer | – |
Match E3SM variables to requested outputs#
Expand the table to include columns that indicate E3SM model variable names and any conversion factor.
# drop comments
vars_mean_list = vars_mean_list.drop(columns='comment (reported at end of each model physics time step, green=minimum, red=granularity enabling EMC2)')
# 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)
# match to E3SM variable names and specify conversion factors
for index in vars_mean_list.index:
standard_name = vars_mean_list.standard_name.iat[index]
if standard_name=='air_pressure':
vars_mean_list.model_name.iat[index] = 'pa'
if standard_name=='layer_top_pressure':
vars_mean_list.model_name.iat[index] = 'pe'
if standard_name=='surface_pressure':
vars_mean_list.model_name.iat[index] = 'ps'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='surface_temperature':
vars_mean_list.model_name.iat[index] = 'ts'
if standard_name=='surface_friction_velocity':
vars_mean_list.model_name.iat[index] = 'ustar'
# if standard_name=='surface_roughness_length_for_momentum_in_air':
# vars_mean_list.model_name.iat[index] = 'z0m'
# if standard_name=='surface_roughness_length_for_heat_in_air':
# vars_mean_list.model_name.iat[index] = 'z0h'
# if standard_name=='surface_roughness_length_for_humidity_in_air':
# vars_mean_list.model_name.iat[index] = 'z0q'
if standard_name=='surface_upward_sensible_heat_flux':
vars_mean_list.model_name.iat[index] = 'hfss'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='surface_upward_latent_heat_flux':
vars_mean_list.model_name.iat[index] = 'hfls'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='obukhov_length':
vars_mean_list.model_name.iat[index] = 'ol'
if standard_name=='pbl_height':
vars_mean_list.model_name.iat[index] = 'pblh'
if standard_name=='inversion_height':
vars_mean_list.model_name.iat[index] = 'zi'
if standard_name=='atmosphere_mass_content_of_liquid_cloud_water':
vars_mean_list.model_name.iat[index] = 'lwpc'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='atmosphere_mass_content_of_rain_water':
vars_mean_list.model_name.iat[index] = 'lwpr'
vars_mean_list.conv_factor.iat[index] = 1.
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.
if standard_name=='area_fraction_cover_of_hydrometeors':
vars_mean_list.model_name.iat[index] = 'cf'
# if standard_name=='area_fraction_cover_of_liquid_cloud':
# vars_mean_list.model_name.iat[index] = ''
if standard_name=='area_fraction_cover_of_convective_hydrometeors':
vars_mean_list.model_name.iat[index] = 'cfc'
# if standard_name=='optical_depth':
# vars_mean_list.model_name.iat[index] = 'od'
# if standard_name=='optical_depth_of_liquid_water':
# vars_mean_list.model_name.iat[index] = 'odlc'
if standard_name=='precipitation_flux_at_surface':
vars_mean_list.model_name.iat[index] = 'pr'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='precipitation_flux_of_ice_at_surface':
vars_mean_list.model_name.iat[index] = 'pri'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='toa_outgoing_longwave_flux':
vars_mean_list.model_name.iat[index] = 'rlut'
if standard_name=='surface_downwelling_longwave_flux':
vars_mean_list.model_name.iat[index] = 'rlds'
if standard_name=='surface_upwelling_longwave_flux':
vars_mean_list.model_name.iat[index] = 'rlus'
if standard_name=='height':
vars_mean_list.model_name.iat[index] = 'zf'
if standard_name=='eastward_wind':
vars_mean_list.model_name.iat[index] = 'ua'
if standard_name=='northward_wind':
vars_mean_list.model_name.iat[index] = 'va'
if standard_name=='air_dry_density':
vars_mean_list.model_name.iat[index] = 'rhoa'
if standard_name=='air_temperature':
vars_mean_list.model_name.iat[index] = 'ta'
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] = 'hur'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='relative_humidity_over_ice':
vars_mean_list.model_name.iat[index] = 'huri'
vars_mean_list.conv_factor.iat[index] = 1.
if standard_name=='air_potential_temperature':
vars_mean_list.model_name.iat[index] = 'theta'
if standard_name=='mass_mixing_ratio_of_cloud_liquid_water_in_air':
vars_mean_list.model_name.iat[index] = 'qlc'
if standard_name=='mass_mixing_ratio_of_rain_water_in_air':
vars_mean_list.model_name.iat[index] = 'qlr'
if do_ice:
if standard_name=='mass_mixing_ratio_of_ice_water_in_air':
vars_mean_list.model_name.iat[index] = 'qi'
if standard_name=='area_fraction_of_hydrometeors':
vars_mean_list.model_name.iat[index] = 'fh'
if standard_name=='area_fraction_of_liquid_cloud':
vars_mean_list.model_name.iat[index] = 'flc'
if standard_name=='area_fraction_of_convective_hydrometeors':
vars_mean_list.model_name.iat[index] = 'fc'
# if standard_name=='precipitation_flux_in_air':
# vars_mean_list.model_name.iat[index] = 'prt'
# vars_mean_list.conv_factor.iat[index] = 1./86400
# if do_ice:
# if standard_name=='precipitation_flux_in_air_in_ice_phase':
# vars_mean_list.model_name.iat[index] = 'pit'
# vars_mean_list.conv_factor.iat[index] = 1./86400
if standard_name=='specific_turbulent_kinetic_energy':
vars_mean_list.model_name.iat[index] = 'tke'
# if standard_name=='disspation_rate_of_turbulent_kinetic_energy':
# vars_mean_list.model_name.iat[index] = 'dissip_tke_turb'
# vars_mean_list.conv_factor.iat[index] = -1.
if standard_name=='zonal_momentum_flux':
vars_mean_list.model_name.iat[index] = 'uw'
if standard_name=='meridional_momentum_flux':
vars_mean_list.model_name.iat[index] = 'vw'
if standard_name=='variance_of_upward_air_velocity':
vars_mean_list.model_name.iat[index] = 'w2'
# if standard_name=='vertical_flux_potential_temperature':
# vars_mean_list.model_name.iat[index] = 'wth_turb'
# if standard_name=='vertical_flux_liquid_water_potential_temperature':
# vars_mean_list.model_name.iat[index] = ''
# if standard_name=='vertical_flux_water_vapor':
# vars_mean_list.model_name.iat[index] = 'wq_turb'
# if standard_name=='vertical_flux_total_water':
# vars_mean_list.model_name.iat[index] = 'wqt_turb'
# if standard_name=='convection_updraft_mass_flux':
# vars_mean_list.model_name.iat[index] = 'lwdp'
# if standard_name=='convection_downdraft_mass_flux':
# vars_mean_list.model_name.iat[index] = 'lwdp'
if standard_name=='downwelling_longwave_flux_in_air':
vars_mean_list.model_name.iat[index] = 'rld'
if standard_name=='upwelling_longwave_flux_in_air':
vars_mean_list.model_name.iat[index] = 'rlu'
# if standard_name=='tendency_of_air_potential_temperature_due_to_radiative_heating':
# vars_mean_list.model_name.iat[index] = 'dth_lw'
# vars_mean_list.conv_factor.iat[index] = 1./86400
# 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./86400
# if standard_name=='tendency_of_air_potential_temperature_due_to_mixing':
# vars_mean_list.model_name.iat[index] = 'dth_turb'
# vars_mean_list.conv_factor.iat[index] = 1./86400
# 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./86400
# if standard_name=='tendency_of_water_vapor_mixing_ratio_due_to_mixing':
# vars_mean_list.model_name.iat[index] = 'dq_turb'
# vars_mean_list.conv_factor.iat[index] = 1./86400
if standard_name=='mass_mixing_ratio_of_liquid_cloud_water_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'qlcs'
if standard_name=='mass_mixing_ratio_of_rain_water_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'qlrs'
if do_ice:
if standard_name=='mass_mixing_ratio_of_ice_cloud_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'qics'
# if standard_name=='mass_mixing_ratio_of_ice_precipitation_in_air_stratiform':
# vars_mean_list.model_name.iat[index] = 'qips' # Not for P3
# if standard_name=='mass_mixing_ratio_of_liquid_cloud_water_in_air_convective':
# vars_mean_list.model_name.iat[index] = 'QCLmc'
# if standard_name=='mass_mixing_ratio_of_rain_water_in_air_convective':
# vars_mean_list.model_name.iat[index] = 'QPLmc'
# if do_ice:
# if standard_name=='mass_mixing_ratio_of_ice_cloud_in_air_convective':
# vars_mean_list.model_name.iat[index] = 'QCImc'
# if standard_name=='mass_mixing_ratio_of_ice_precipitation_in_air_convective':
# vars_mean_list.model_name.iat[index] = 'QPImc'
if standard_name=='number_of_liquid_cloud_droplets_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'nlcs'
if standard_name=='number_of_rain_drops_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'nlrs'
if do_ice:
if standard_name=='number_of_ice_cloud_crystals_in_air_stratiform':
vars_mean_list.model_name.iat[index] = 'nics'
# if standard_name=='number_of_ice_precipitation_crystals_in_air_stratiform':
# vars_mean_list.model_name.iat[index] = 'nips' # Not for P3
# if standard_name=='effective_radius_of_liquid_cloud_droplets_convective':
# vars_mean_list.model_name.iat[index] = 're_mccl'
# if standard_name=='effective_radius_of_rain_convective':
# vars_mean_list.model_name.iat[index] = 're_mcpl'
# if do_ice:
# if standard_name=='effective_radius_of_ice_cloud_convective':
# vars_mean_list.model_name.iat[index] = 're_mcci'
# if standard_name=='effective_radius_of_ice_precipitation_convective':
# vars_mean_list.model_name.iat[index] = 're_mcpi'
if standard_name=='area_fraction_of_liquid_cloud_stratiform':
vars_mean_list.model_name.iat[index] = 'flcs'
if standard_name=='area_fraction_of_rain_stratiform':
vars_mean_list.model_name.iat[index] = 'flrs'
if do_ice:
if standard_name=='area_fraction_of_ice_cloud_stratiform':
vars_mean_list.model_name.iat[index] = 'fics'
# if standard_name=='area_fraction_of_ice_precipitation_stratiform':
# vars_mean_list.model_name.iat[index] = 'fips' # Not for P3
# if standard_name=='area_fraction_of_liquid_cloud_convective':
# vars_mean_list.model_name.iat[index] = 'cldmccl'
# if standard_name=='area_fraction_of_rain_convective':
# vars_mean_list.model_name.iat[index] = 'cldmcpl'
# if do_ice:
# if standard_name=='area_fraction_of_ice_cloud_convective':
# vars_mean_list.model_name.iat[index] = 'cldmcci'
# if standard_name=='area_fraction_of_ice_precipitation_convective':
# vars_mean_list.model_name.iat[index] = 'cldmcpi'
# Not for P3
# if standard_name=='mass_weighted_fall_speed_of_liquid_cloud_water_stratiform':
# vars_mean_list.model_name.iat[index] = 'vmlcs'
# if standard_name=='mass_weighted_fall_speed_of_rain_stratiform':
# vars_mean_list.model_name.iat[index] = 'vmlrs'
# if do_ice:
# if standard_name=='mass_weighted_fall_speed_of_ice_cloud_stratiform':
# vars_mean_list.model_name.iat[index] = 'vmics'
# if standard_name=='mass_weighted_fall_speed_of_ice_precipitation_stratiform':
# vars_mean_list.model_name.iat[index] = 'vmips'
# Not for P3
# if standard_name=='mass_weighted_fall_speed_of_liquid_cloud_water_convective':
# vars_mean_list.model_name.iat[index] = 'vm_mccl'
# if standard_name=='mass_weighted_fall_speed_of_rain_convective':
# vars_mean_list.model_name.iat[index] = 'vm_mcpl'
# if do_ice:
# if standard_name=='mass_weighted_fall_speed_of_cloud_ice_crystals_convective':
# vars_mean_list.model_name.iat[index] = 'vm_mcci'
# if standard_name=='mass_weighted_fall_speed_of_ice_precipitation_convective':
# vars_mean_list.model_name.iat[index] = 'vm_mcpi'
vars_mean_list[:] # echo variables (first two rows are dimensions)
| standard_name | variable_id | units | dimensions | model_name | conv_factor | |
|---|---|---|---|---|---|---|
| 0 | time | time | s | – | missing data | 1.0 |
| 1 | pressure_layer | layer | 1 | – | missing data | 1.0 |
| 2 | air_pressure | pa | Pa | time, layer | pa | 1.0 |
| 3 | layer_top_pressure | pe | Pa | time, layer | pe | 1.0 |
| 4 | surface_pressure | ps | Pa | time | ps | 1.0 |
| 5 | surface_temperature | ts | K | time | ts | 1.0 |
| 6 | surface_friction_velocity | ustar | m s-1 | time | ustar | 1.0 |
| 7 | surface_roughness_length_for_momentum_in_air | z0 | m | time | missing data | 1.0 |
| 8 | surface_roughness_length_for_heat_in_air | z0h | m | time | missing data | 1.0 |
| 9 | surface_roughness_length_for_humidity_in_air | z0q | m | time | missing data | 1.0 |
| 10 | surface_upward_sensible_heat_flux | hfss | W m-2 | time | hfss | 1.0 |
| 11 | surface_upward_latent_heat_flux | hfls | W m-2 | time | hfls | 1.0 |
| 12 | obukhov_length | ol | m | time | ol | 1.0 |
| 13 | pbl_height | pblh | m | time | pblh | 1.0 |
| 14 | inversion_height | zi | m | time | zi | 1.0 |
| 15 | atmosphere_mass_content_of_liquid_cloud_water | lwpc | kg m-2 | time | lwpc | 1.0 |
| 16 | atmosphere_mass_content_of_rain_water | lwpr | kg m-2 | time | lwpr | 1.0 |
| 17 | atmosphere_mass_content_of_ice_water | iwp | kg m-2 | time | missing data | 1.0 |
| 18 | area_fraction_cover_of_hydrometeors | cf | 1 | time | cf | 1.0 |
| 19 | area_fraction_cover_of_liquid_cloud | cflc | 1 | time | missing data | 1.0 |
| 20 | area_fraction_cover_of_convective_hydrometeors | cfc | 1 | time | cfc | 1.0 |
| 21 | optical_depth | od | 1 | time | missing data | 1.0 |
| 22 | optical_depth_of_liquid_cloud | odlc | 1 | time | missing data | 1.0 |
| 23 | precipitation_flux_at_surface | pr | kg m-2 s-1 | time | pr | 1.0 |
| 24 | precipitation_flux_of_ice_at_surface | pri | kg m-2 s-1 | time | pri | 1.0 |
| 25 | toa_outgoing_longwave_flux | rlut | W m-2 | time | rlut | 1.0 |
| 26 | surface_downwelling_longwave_flux | rlds | W m-2 | time | rlds | 1.0 |
| 27 | surface_upwelling_longwave_flux | rlus | W m-2 | time | rlus | 1.0 |
| 28 | surface_sea_spray_number_flux | ssaf | m-2 s-1 | time | missing data | 1.0 |
| 29 | height | zf | m | time, layer | zf | 1.0 |
| 30 | eastward_wind | ua | m s-1 | time, layer | ua | 1.0 |
| 31 | northward_wind | va | m s-1 | time, layer | va | 1.0 |
| 32 | air_dry_density | rhoa | kg m-3 | time, layer | rhoa | 1.0 |
| 33 | air_temperature | ta | K | time, layer | ta | 1.0 |
| 34 | water_vapor_mixing_ratio | qv | kg kg-1 | time, layer | qv | 1.0 |
| 35 | relative_humidity | hur | 1 | time, layer | hur | 1.0 |
| 36 | relative_humidity_over_ice | huri | 1 | time, layer | huri | 1.0 |
| 37 | air_potential_temperature | theta | K | time, layer | theta | 1.0 |
| 38 | mass_mixing_ratio_of_cloud_liquid_water_in_air | qlc | kg kg-1 | time, layer | qlc | 1.0 |
| 39 | mass_mixing_ratio_of_rain_water_in_air | qlr | kg kg-1 | time, layer | qlr | 1.0 |
| 40 | mass_mixing_ratio_of_ice_water_in_air | qi | kg kg-1 | time, layer | missing data | 1.0 |
| 41 | area_fraction_of_hydrometeors | fh | 1 | time, layer | fh | 1.0 |
| 42 | area_fraction_of_liquid_cloud | flc | 1 | time, layer | flc | 1.0 |
| 43 | area_fraction_of_convective_hydrometeors | fc | 1 | time, layer | fc | 1.0 |
| 44 | precipitation_flux_in_air | prf | kg m-2 s-1 | time, layer | missing data | 1.0 |
| 45 | precipitation_flux_in_air_in_ice_phase | prfi | kg m-2 s-1 | time, layer | missing data | 1.0 |
| 46 | specific_turbulent_kinetic_energy | tke | m2 s-2 | time, layer | tke | 1.0 |
| 47 | dissipation_rate_of_turbulent_kinetic_energy | eps | m2 s-3 | time, layer | missing data | 1.0 |
| 48 | zonal_momentum_flux | uw | m2 s-2 | time, layer | uw | 1.0 |
| 49 | meridional_momentum_flux | vw | m2 s-2 | time, layer | vw | 1.0 |
| 50 | variance_of_upward_air_velocity | w2 | m2 s-2 | time, layer | w2 | 1.0 |
| 51 | vertical_flux_potential_temperature | wth | K m s-1 | time, layer | missing data | 1.0 |
| 52 | vertical_flux_liquid_ice_water_potential_tempe... | vf_thli | K m s-1 | time, layer | missing data | 1.0 |
| 53 | vertical_flux_water_vapor | wqv | kg kg-1 m s-1 | time, layer | missing data | 1.0 |
| 54 | vertical_flux_total_water | vf_qt | kg kg-1 m s-1 | time, layer | missing data | 1.0 |
| 55 | convection_updraft_mass_flux | cmfu | kg m-2 s-1 | time, layer | missing data | 1.0 |
| 56 | convection_downdraft_mass_flux | cmfd | kg m-2 s-1 | time, layer | missing data | 1.0 |
| 57 | downwelling_longwave_flux_in_air | rld | W m-2 | time, layer | rld | 1.0 |
| 58 | upwelling_longwave_flux_in_air | rlu | W m-2 | time, layer | rlu | 1.0 |
| 59 | tendency_of_air_potential_temperature_due_to_r... | dth_rad | K s-1 | time, layer | missing data | 1.0 |
| 60 | tendency_of_air_potential_temperature_due_to_m... | dth_micro | K s-1 | time, layer | missing data | 1.0 |
| 61 | tendency_of_air_potential_temperature_due_to_m... | dth_turb | K s-1 | time, layer | missing data | 1.0 |
| 62 | tendency_of_water_vapor_mixing_ratio_due_to_mi... | dq_micro | s-1 | time, layer | missing data | 1.0 |
| 63 | tendency_of_water_vapor_mixing_ratio_due_to_mi... | dq_turb | s-1 | time, layer | missing data | 1.0 |
| 64 | number_of_total_aerosol_mode1 | na1 | kg-1 | time, layer | missing data | 1.0 |
| 65 | number_of_total_aerosol_mode2 | na2 | kg-1 | time, layer | missing data | 1.0 |
| 66 | number_of_total_aerosol_mode3 | na3 | kg-1 | time, layer | missing data | 1.0 |
| 67 | tendency_of_aerosol_number_due_to_warm_microph... | dna_micro_warm | kg-1 s-1 | time, layer | missing data | 1.0 |
| 68 | tendency_of_aerosol_number_due_to_cold_microph... | dna_micro_cold | kg-1 s-1 | time, layer | missing data | 1.0 |
| 69 | tendency_of_aerosol_number_due_to_mixing | dna_turb | kg-1 s-1 | time, layer | missing data | 1.0 |
| 70 | tendency_of_ice_number_due_to_heterogeneous_fr... | dni_het | kg-1 s-1 | time, layer | missing data | 1.0 |
| 71 | tendency_of_ice_number_due_to_secondary_ice_pr... | dni_sip | kg-1 s-1 | time, layer | missing data | 1.0 |
| 72 | tendency_of_ice_number_due_to_homogeneous_free... | dni_hom | kg-1 s-1 | time, layer | missing data | 1.0 |
| 73 | mass_mixing_ratio_of_liquid_cloud_water_in_air... | qlcs | kg kg-1 | time, layer | qlcs | 1.0 |
| 74 | mass_mixing_ratio_of_rain_water_in_air_stratiform | qlrs | kg kg-1 | time, layer | qlrs | 1.0 |
| 75 | mass_mixing_ratio_of_ice_cloud_in_air_stratiform | qics | kg kg-1 | time, layer | missing data | 1.0 |
| 76 | mass_mixing_ratio_of_ice_precipitation_in_air_... | qips | kg kg-1 | time, layer | missing data | 1.0 |
| 77 | mass_mixing_ratio_of_liquid_cloud_water_in_air... | qlcc | kg kg-1 | time, layer | missing data | 1.0 |
| 78 | mass_mixing_ratio_of_rain_water_in_air_convective | qlrc | kg kg-1 | time, layer | missing data | 1.0 |
| 79 | mass_mixing_ratio_of_ice_cloud_in_air_convective | qicc | kg kg-1 | time, layer | missing data | 1.0 |
| 80 | mass_mixing_ratio_of_ice_precipitation_in_air_... | qipc | kg kg-1 | time, layer | missing data | 1.0 |
| 81 | number_of_liquid_cloud_droplets_in_air_stratiform | nlcs | kg-1 | time, layer | nlcs | 1.0 |
| 82 | number_of_rain_drops_in_air_stratiform | nlrs | kg-1 | time, layer | nlrs | 1.0 |
| 83 | number_of_ice_cloud_crystals_in_air_stratiform | nics | kg-1 | time, layer | missing data | 1.0 |
| 84 | number_of_ice_precipitation_crystals_in_air_st... | nips | kg-1 | time, layer | missing data | 1.0 |
| 85 | effective_radius_of_liquid_cloud_droplets_conv... | relcc | m | time, layer | missing data | 1.0 |
| 86 | effective_radius_of_rain_convective | relrc | m | time, layer | missing data | 1.0 |
| 87 | effective_radius_of_ice_cloud_convective | reicc | m | time, layer | missing data | 1.0 |
| 88 | effective_radius_of_ice_precipitation_convective | reipc | m | time, layer | missing data | 1.0 |
| 89 | area_fraction_of_liquid_cloud_stratiform | flcs | 1 | time, layer | flcs | 1.0 |
| 90 | area_fraction_of_rain_stratiform | flrs | 1 | time, layer | flrs | 1.0 |
| 91 | area_fraction_of_ice_cloud_stratiform | fics | 1 | time, layer | missing data | 1.0 |
| 92 | area_fraction_of_ice_precipitation_stratiform | fips | 1 | time, layer | missing data | 1.0 |
| 93 | area_fraction_of_liquid_cloud_convective | flcc | 1 | time, layer | missing data | 1.0 |
| 94 | area_fraction_of_rain_convective | flrc | 1 | time, layer | missing data | 1.0 |
| 95 | area_fraction_of_ice_cloud_convective | ficc | 1 | time, layer | missing data | 1.0 |
| 96 | area_fraction_of_ice_precipitation_convective | fipc | 1 | time, layer | missing data | 1.0 |
| 97 | mass_weighted_fall_speed_of_liquid_cloud_water... | vmlcs | m s-1 | time, layer | missing data | 1.0 |
| 98 | mass_weighted_fall_speed_of_rain_stratiform | vmlrs | m s-1 | time, layer | missing data | 1.0 |
| 99 | mass_weighted_fall_speed_of_ice_cloud_stratiform | vmics | m s-1 | time, layer | missing data | 1.0 |
| 100 | mass_weighted_fall_speed_of_ice_precipitation_... | vmips | m s-1 | time, layer | missing data | 1.0 |
| 101 | mass_weighted_fall_speed_of_liquid_cloud_water... | vmlcc | m s-1 | time, layer | missing data | 1.0 |
| 102 | mass_weighted_fall_speed_of_rain_convective | vmlrc | m s-1 | time, layer | missing data | 1.0 |
| 103 | mass_weighted_fall_speed_of_cloud_ice_crystals... | vmicc | m s-1 | time, layer | missing data | 1.0 |
| 104 | mass_weighted_fall_speed_of_ice_precipitation_... | vmipc | m s-1 | time, layer | missing data | 1.0 |
Create DEPHY output file#
Write a single file to contain all domain-mean scalar and profile outputs. This code expects the write directory to be pre-existing (already created by the user). In the case that this output will be committed to the comble-mip GitHub repository, see above “Specify directory locations”.
# create DEPHY output file
dephy_filename = my_gitdir + my_output_filename
if os.path.exists(dephy_filename):
os.remove(dephy_filename)
print('The file ' + dephy_filename + ' has been deleted successfully')
dephy_file = Dataset(dephy_filename,mode='w',format='NETCDF3_CLASSIC')
start_date = '2020-03-12T22:00:00Z'
# create global attributes
dephy_file.title='E3SMv3.0 development version SCM results for COMBLE-MIP case: fixed stratiform Nd without ice'
dephy_file.reference='https://github.com/ARM-Development/comble-mip'
dephy_file.authors='Meng Zhang (zhang55@llnl.gov), Xue Zheng (zheng7@llnl.gov), Peter Bogenschutz (bogenschutz1@llnl.gov), Yunyan Zhang (zhang25@llnl.gov), Shaocheng Xie (xie2@llnl.gov)'
dephy_file.source=input_filename
dephy_file.version=dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
dephy_file.format_version='DEPHY SCM format version 1.6'
dephy_file.script='convert_E3SMv3_SCM_output_to_dephy_format.ipynb'
dephy_file.startDate=start_date
dephy_file.force_geo=1
dephy_file.surfaceType='ocean'
dephy_file.surfaceForcing='ts'
dephy_file.lat='74.5 deg N'
dephy_file.dp='see pressure variable'
dephy_file.np=model_data.sizes['lev']
dephy_file.Note='E3SMv3 development version of SCM run without the usage of geostrophic wind.'
# create dimensions
nt = model_data.dims['time']
time = dephy_file.createDimension('time', nt)
time = dephy_file.createVariable('time', np.float64, ('time',))
time.units = 'seconds since ' + dephy_file.startDate
time.long_name = 'time'
# find time step and build time in seconds
# time1 = dt.datetime.strptime(str(model_data['time'].data[0]),'%Y-%m-%d %H:%M:%S')
# time2 = dt.datetime.strptime(str(model_data['time'].data[1]),'%Y-%m-%d %H:%M:%S')
time1 = model_data['time'].data[0]
time2 = model_data['time'].data[1]
delta_t = (time2-time1).astype('timedelta64[s]') #.total_seconds()
time[:] = (np.arange(nt)+1.)*delta_t
nl = model_data.dims['lev']
pa = dephy_file.createDimension('layer', nl)
pa = dephy_file.createVariable('layer', np.float64, ('layer',))
pa.units = 'Pa'
pa.long_name = 'pressure'
pa[:] = model_data['lev'].data*100.
# create and fill variables
for index in vars_mean_list.index[2:]:
std_name = vars_mean_list.standard_name.iat[index]
print(std_name) # debug
var_name = vars_mean_list.variable_id.iat[index]
mod_name = vars_mean_list.model_name.iat[index]
c_factor = vars_mean_list.conv_factor.iat[index]
if vars_mean_list.dimensions.iat[index]=='time':
new_sca = dephy_file.createVariable(var_name, np.float64, ('time'))
new_sca.units = vars_mean_list.units.iat[index]
new_sca.long_name = std_name
if vars_mean_list.model_name.iat[index]!='missing data':
new_sca[:] = model_data[mod_name].data*c_factor
if vars_mean_list.dimensions.iat[index]=='time, layer':
new_snd = dephy_file.createVariable(var_name, np.float64, ('time','layer'))
new_snd.units = vars_mean_list.units.iat[index]
new_snd.long_name = std_name
if vars_mean_list.model_name.iat[index]!='missing data':
new_snd[:] = model_data[mod_name].data*c_factor
print(dephy_file)
dephy_file.close()
The file /home/mzhang1/Data_test/COMBLE-MIP-SCM/gitdir/E3SMv3dev-Phys_FixN_noice_def_z0_alt_no_ugvg.nc has been deleted successfully
air_pressure
layer_top_pressure
surface_pressure
surface_temperature
surface_friction_velocity
surface_roughness_length_for_momentum_in_air
surface_roughness_length_for_heat_in_air
surface_roughness_length_for_humidity_in_air
surface_upward_sensible_heat_flux
surface_upward_latent_heat_flux
obukhov_length
pbl_height
inversion_height
atmosphere_mass_content_of_liquid_cloud_water
atmosphere_mass_content_of_rain_water
atmosphere_mass_content_of_ice_water
area_fraction_cover_of_hydrometeors
area_fraction_cover_of_liquid_cloud
area_fraction_cover_of_convective_hydrometeors
optical_depth
optical_depth_of_liquid_cloud
precipitation_flux_at_surface
precipitation_flux_of_ice_at_surface
toa_outgoing_longwave_flux
surface_downwelling_longwave_flux
surface_upwelling_longwave_flux
surface_sea_spray_number_flux
height
eastward_wind
northward_wind
air_dry_density
air_temperature
water_vapor_mixing_ratio
relative_humidity
relative_humidity_over_ice
air_potential_temperature
mass_mixing_ratio_of_cloud_liquid_water_in_air
mass_mixing_ratio_of_rain_water_in_air
mass_mixing_ratio_of_ice_water_in_air
area_fraction_of_hydrometeors
area_fraction_of_liquid_cloud
area_fraction_of_convective_hydrometeors
precipitation_flux_in_air
precipitation_flux_in_air_in_ice_phase
specific_turbulent_kinetic_energy
dissipation_rate_of_turbulent_kinetic_energy
zonal_momentum_flux
meridional_momentum_flux
variance_of_upward_air_velocity
vertical_flux_potential_temperature
vertical_flux_liquid_ice_water_potential_temperature
vertical_flux_water_vapor
vertical_flux_total_water
convection_updraft_mass_flux
convection_downdraft_mass_flux
downwelling_longwave_flux_in_air
upwelling_longwave_flux_in_air
tendency_of_air_potential_temperature_due_to_radiative_heating
tendency_of_air_potential_temperature_due_to_microphysics
tendency_of_air_potential_temperature_due_to_mixing
tendency_of_water_vapor_mixing_ratio_due_to_microphysics
tendency_of_water_vapor_mixing_ratio_due_to_mixing
number_of_total_aerosol_mode1
number_of_total_aerosol_mode2
number_of_total_aerosol_mode3
tendency_of_aerosol_number_due_to_warm_microphysics
tendency_of_aerosol_number_due_to_cold_microphysics
tendency_of_aerosol_number_due_to_mixing
tendency_of_ice_number_due_to_heterogeneous_freezing
tendency_of_ice_number_due_to_secondary_ice_production
tendency_of_ice_number_due_to_homogeneous_freezing
mass_mixing_ratio_of_liquid_cloud_water_in_air_stratiform
mass_mixing_ratio_of_rain_water_in_air_stratiform
mass_mixing_ratio_of_ice_cloud_in_air_stratiform
mass_mixing_ratio_of_ice_precipitation_in_air_stratiform
mass_mixing_ratio_of_liquid_cloud_water_in_air_convective
mass_mixing_ratio_of_rain_water_in_air_convective
mass_mixing_ratio_of_ice_cloud_in_air_convective
mass_mixing_ratio_of_ice_precipitation_in_air_convective
number_of_liquid_cloud_droplets_in_air_stratiform
number_of_rain_drops_in_air_stratiform
number_of_ice_cloud_crystals_in_air_stratiform
number_of_ice_precipitation_crystals_in_air_stratiform
effective_radius_of_liquid_cloud_droplets_convective
effective_radius_of_rain_convective
effective_radius_of_ice_cloud_convective
effective_radius_of_ice_precipitation_convective
area_fraction_of_liquid_cloud_stratiform
area_fraction_of_rain_stratiform
area_fraction_of_ice_cloud_stratiform
area_fraction_of_ice_precipitation_stratiform
area_fraction_of_liquid_cloud_convective
area_fraction_of_rain_convective
area_fraction_of_ice_cloud_convective
area_fraction_of_ice_precipitation_convective
mass_weighted_fall_speed_of_liquid_cloud_water_stratiform
mass_weighted_fall_speed_of_rain_stratiform
mass_weighted_fall_speed_of_ice_cloud_stratiform
mass_weighted_fall_speed_of_ice_precipitation_stratiform
mass_weighted_fall_speed_of_liquid_cloud_water_convective
mass_weighted_fall_speed_of_rain_convective
mass_weighted_fall_speed_of_cloud_ice_crystals_convective
mass_weighted_fall_speed_of_ice_precipitation_convective
<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_CLASSIC data model, file format NETCDF3):
title: E3SMv3.0 development version SCM results for COMBLE-MIP case: fixed stratiform Nd without ice
reference: https://github.com/ARM-Development/comble-mip
authors: Meng Zhang (zhang55@llnl.gov), Xue Zheng (zheng7@llnl.gov), Peter Bogenschutz (bogenschutz1@llnl.gov), Yunyan Zhang (zhang25@llnl.gov), Shaocheng Xie (xie2@llnl.gov)
source: /home/mzhang1/Data_test/COMBLE-MIP-SCM/COMBLE-MIP_v2_4_e3smv3_0_FixN_noice.eam.h0.2020-03-12-79200.nc
version: 2024-06-21 21:40:20
format_version: DEPHY SCM format version 1.6
script: convert_E3SMv3_SCM_output_to_dephy_format.ipynb
startDate: 2020-03-12T22:00:00Z
force_geo: 1
surfaceType: ocean
surfaceForcing: ts
lat: 74.5 deg N
dp: see pressure variable
np: 80
Note: E3SMv3 development version of SCM run without the usage of geostrophic wind.
dimensions(sizes): time(41), layer(80)
variables(dimensions): float64 time(time), float64 layer(layer), float64 pa(time, layer), float64 pe(time, layer), float64 ps(time), float64 ts(time), float64 ustar(time), float64 z0(time), float64 z0h(time), float64 z0q(time), float64 hfss(time), float64 hfls(time), float64 ol(time), float64 pblh(time), float64 zi(time), float64 lwpc(time), float64 lwpr(time), float64 iwp(time), float64 cf(time), float64 cflc(time), float64 cfc(time), float64 od(time), float64 odlc(time), float64 pr(time), float64 pri(time), float64 rlut(time), float64 rlds(time), float64 rlus(time), float64 ssaf(time), float64 zf(time, layer), float64 ua(time, layer), float64 va(time, layer), float64 rhoa(time, layer), float64 ta(time, layer), float64 qv(time, layer), float64 hur(time, layer), float64 huri(time, layer), float64 theta(time, layer), float64 qlc(time, layer), float64 qlr(time, layer), float64 qi(time, layer), float64 fh(time, layer), float64 flc(time, layer), float64 fc(time, layer), float64 prf(time, layer), float64 prfi(time, layer), float64 tke(time, layer), float64 eps(time, layer), float64 uw(time, layer), float64 vw(time, layer), float64 w2(time, layer), float64 wth(time, layer), float64 vf_thli(time, layer), float64 wqv(time, layer), float64 vf_qt(time, layer), float64 cmfu(time, layer), float64 cmfd(time, layer), float64 rld(time, layer), float64 rlu(time, layer), float64 dth_rad(time, layer), float64 dth_micro(time, layer), float64 dth_turb(time, layer), float64 dq_micro(time, layer), float64 dq_turb(time, layer), float64 na1(time, layer), float64 na2(time, layer), float64 na3(time, layer), float64 dna_micro_warm(time, layer), float64 dna_micro_cold(time, layer), float64 dna_turb(time, layer), float64 dni_het(time, layer), float64 dni_sip(time, layer), float64 dni_hom(time, layer), float64 qlcs(time, layer), float64 qlrs(time, layer), float64 qics(time, layer), float64 qips(time, layer), float64 qlcc(time, layer), float64 qlrc(time, layer), float64 qicc(time, layer), float64 qipc(time, layer), float64 nlcs(time, layer), float64 nlrs(time, layer), float64 nics(time, layer), float64 nips(time, layer), float64 relcc(time, layer), float64 relrc(time, layer), float64 reicc(time, layer), float64 reipc(time, layer), float64 flcs(time, layer), float64 flrs(time, layer), float64 fics(time, layer), float64 fips(time, layer), float64 flcc(time, layer), float64 flrc(time, layer), float64 ficc(time, layer), float64 fipc(time, layer), float64 vmlcs(time, layer), float64 vmlrs(time, layer), float64 vmics(time, layer), float64 vmips(time, layer), float64 vmlcc(time, layer), float64 vmlrc(time, layer), float64 vmicc(time, layer), float64 vmipc(time, layer)
groups:
Check output file#
dephy_check = xr.open_dataset(dephy_filename)
print('File name ',dephy_filename)
dephy_check
File name /home/mzhang1/Data_test/COMBLE-MIP-SCM/gitdir/E3SMv3dev-Phys_FixN_noice_def_z0_alt_no_ugvg.nc
<xarray.Dataset>
Dimensions: (time: 41, layer: 80)
Coordinates:
* time (time) datetime64[ns] 2020-03-12T22:30:00 ... 2020-03-13T...
* layer (layer) float64 12.36 18.19 26.74 ... 9.938e+04 9.985e+04
Data variables: (12/103)
pa (time, layer) float64 ...
pe (time, layer) float64 ...
ps (time) float64 ...
ts (time) float64 ...
ustar (time) float64 ...
z0 (time) float64 ...
... ...
vmics (time, layer) float64 ...
vmips (time, layer) float64 ...
vmlcc (time, layer) float64 ...
vmlrc (time, layer) float64 ...
vmicc (time, layer) float64 ...
vmipc (time, layer) float64 ...
Attributes: (12/15)
title: E3SMv3.0 development version SCM results for COMBLE-MIP ...
reference: https://github.com/ARM-Development/comble-mip
authors: Meng Zhang (zhang55@llnl.gov), Xue Zheng (zheng7@llnl.go...
source: /home/mzhang1/Data_test/COMBLE-MIP-SCM/COMBLE-MIP_v2_4_e...
version: 2024-06-21 21:40:20
format_version: DEPHY SCM format version 1.6
... ...
surfaceType: ocean
surfaceForcing: ts
lat: 74.5 deg N
dp: see pressure variable
np: 80
Note: E3SMv3 development version of SCM run without the usage ...