Example: convert ModelE3 SCM output to DEPHY format

Code to read ModelE3 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_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 = 'entppe.nc'
# my_output_suffix = 'Phys_FixN_def_z0.nc'

# Phys without ice
# my_input_suffix = 'entppe_noice.nc'
# my_output_suffix = 'Phys_FixN_noice_def_z0.nc'

# Tun1
# my_input_suffix = 't698ml.nc'
# my_output_suffix = 'Tun1_FixN_def_z0.nc'

# Tun1 without ice
# my_input_suffix = 't698ml_noice.nc'
# my_output_suffix = 'Tun1_FixN_noice_def_z0.nc'

# Tun2
# my_input_suffix = 't705ml.nc'
# my_output_suffix = 'Tun2_FixN_def_z0.nc'

# Tun2 without ice
my_input_suffix = 't705ml_noice.nc'
my_output_suffix = 'Tun2_FixN_noice_def_z0.nc'

# specify local source directories (with subdirectories for spin up over ice and restart over water)
my_input_dir = '/data/home/fridlind/modelE3/'

# specify Github scratch directory where processed model output will be committed (automate later)
my_output_filename = 'ModelE3-' + my_output_suffix
my_gitdir = '../../output_scm/modelE/sandbox/'

Read ModelE3 data

Read single file containing all output data

Note: ERROR 1: PROJ… message can be ignored here.

input_filename = my_input_dir + '/allsteps.allmergeSCM_COMBLE-MIP_' + my_input_suffix
modele_data = xr.open_dataset(input_filename)

# check if the run contains ice variables
do_ice = bool(max(modele_data['iwp'].values)>0.)
print('do_ice = ',do_ice)

# full parameter list
modele_data

ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed

do_ice =  False

<xarray.Dataset> Size: 1MB
Dimensions:          (lon: 1, lat: 1, time: 40, p: 110)
Coordinates:
  * lon              (lon) float32 4B 10.0
  * lat              (lat) float32 4B 74.5
  * time             (time) object 320B 2020-03-12 22:15:00 ... 2020-03-13 17...
  * p                (p) float32 440B 979.0 969.0 959.0 ... 0.014 0.0075 0.0035
Data variables: (12/100)
    axyp             (lat, lon) float32 4B ...
    prsurf           (time, lat, lon) float32 160B ...
    gtempr           (time, lat, lon) float32 160B ...
    shflx            (time, lat, lon) float32 160B ...
    lhflx            (time, lat, lon) float32 160B ...
    ustar            (time, lat, lon) float32 160B ...
    ...               ...
    vm_ssci          (time, p, lat, lon) float32 18kB ...
    vm_sspi          (time, p, lat, lon) float32 18kB ...
    vm_mccl          (time, p, lat, lon) float32 18kB ...
    vm_mcpl          (time, p, lat, lon) float32 18kB ...
    vm_mcci          (time, p, lat, lon) float32 18kB ...
    vm_mcpi          (time, p, lat, lon) float32 18kB ...
Attributes:
    xlabel:   SCM_COMBLE-MIP_t705ml_noice COMBLE-MIP Ent PPE (CAO case using ...

xarray.Dataset

• Dimensions:
  • lon: 1
  • lat: 1
  • time: 40
  • p: 110
• Coordinates: (4)
  • lon
    (lon)
    float32
    10.0

    units :

    degrees_east

    array([10.], dtype=float32)

  • lat
    (lat)
    float32
    74.5

    units :

    degrees_north

    array([74.5], dtype=float32)

  • time
    (time)
    object
    2020-03-12 22:15:00 ... 2020-03-...

    array([cftime.DatetimeNoLeap(2020, 3, 12, 22, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 12, 22, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 12, 23, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 12, 23, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 0, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 0, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 1, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 1, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 2, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 2, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 3, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 3, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 4, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 4, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 5, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 5, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 6, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 6, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 7, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 7, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 8, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 8, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 9, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 9, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 10, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 10, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 11, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 11, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 12, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 12, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 13, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 13, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 14, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 14, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 15, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 15, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 16, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 16, 45, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 17, 15, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 3, 13, 17, 45, 0, 0, has_year_zero=True)],
          dtype=object)

  • p
    (p)
    float32
    979.0 969.0 959.0 ... 0.0075 0.0035

    array([9.79000e+02, 9.69000e+02, 9.59000e+02, 9.49000e+02, 9.39000e+02,
           9.29000e+02, 9.19000e+02, 9.09000e+02, 8.99000e+02, 8.88500e+02,
           8.77000e+02, 8.64500e+02, 8.51000e+02, 8.36500e+02, 8.21500e+02,
           8.06500e+02, 7.91500e+02, 7.76500e+02, 7.61500e+02, 7.46500e+02,
           7.31500e+02, 7.16500e+02, 7.01000e+02, 6.84500e+02, 6.67000e+02,
           6.48500e+02, 6.29000e+02, 6.08500e+02, 5.87000e+02, 5.65250e+02,
           5.44000e+02, 5.23250e+02, 5.03000e+02, 4.83250e+02, 4.64000e+02,
           4.45250e+02, 4.27000e+02, 4.09250e+02, 3.92000e+02, 3.75250e+02,
           3.59000e+02, 3.43250e+02, 3.28000e+02, 3.13250e+02, 2.99000e+02,
           2.85250e+02, 2.72000e+02, 2.59250e+02, 2.47000e+02, 2.35250e+02,
           2.24000e+02, 2.13250e+02, 2.03000e+02, 1.93000e+02, 1.83000e+02,
           1.73000e+02, 1.63000e+02, 1.53165e+02, 1.43665e+02, 1.34500e+02,
           1.25665e+02, 1.17165e+02, 1.09000e+02, 1.01165e+02, 9.36650e+01,
           8.65000e+01, 7.96650e+01, 7.31650e+01, 6.70000e+01, 6.11650e+01,
           5.56650e+01, 5.05000e+01, 4.56650e+01, 4.11650e+01, 3.70000e+01,
           3.31650e+01, 2.96650e+01, 2.65000e+01, 2.36000e+01, 2.09000e+01,
           1.84000e+01, 1.61000e+01, 1.40000e+01, 1.21000e+01, 1.04000e+01,
           8.90000e+00, 7.60000e+00, 6.50000e+00, 5.55000e+00, 4.70800e+00,
           3.95100e+00, 3.32600e+00, 2.80650e+00, 2.36250e+00, 1.99400e+00,
           1.66850e+00, 1.39450e+00, 1.14700e+00, 9.32000e-01, 7.41000e-01,
           5.63000e-01, 3.96000e-01, 2.47000e-01, 1.39000e-01, 7.80000e-02,
           4.40000e-02, 2.50000e-02, 1.40000e-02, 7.50000e-03, 3.50000e-03],
          dtype=float32)

• Data variables: (100)
  • axyp
    (lat, lon)
    float32
    ...

    units :

    m^2

    long_name :

    gridcell area

    [1 values with dtype=float32]

  • prsurf
    (time, lat, lon)
    float32
    ...

    units :

    hPa (mb)

    long_name :

    SURFACE PRESSURE

    mip_name :

    ps

    mip_units :

    Pa

    [40 values with dtype=float32]

  • gtempr
    (time, lat, lon)
    float32
    ...

    units :

    K

    long_name :

    SKIN RADIATIVE TEMPERATURE

    mip_name :

    tr

    [40 values with dtype=float32]

  • shflx
    (time, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    SENSIBLE HEAT FLUX

    mip_name :

    hfss

    mip_units :

    W m-2

    [40 values with dtype=float32]

  • lhflx
    (time, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    LATENT HEAT FLUX

    mip_name :

    hfls

    mip_units :

    W m-2

    [40 values with dtype=float32]

  • ustar
    (time, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    FRICTION VELOCITY

    [40 values with dtype=float32]

  • lmonin
    (time, lat, lon)
    float32
    ...

    units :

    m

    long_name :

    Monin-Obukhov length

    [40 values with dtype=float32]

  • pblht_bp
    (time, lat, lon)
    float32
    ...

    units :

    m

    long_name :

    PBL height per lowest mixed layer

    [40 values with dtype=float32]

  • pblht_th
    (time, lat, lon)
    float32
    ...

    units :

    m

    long_name :

    PBL height from potential temperature gradient

    [40 values with dtype=float32]

  • pwv
    (time, lat, lon)
    float32
    ...

    units :

    kg/m^2

    long_name :

    precipitable water vapor

    mip_name :

    prw

    mip_units :

    kg m-2

    [40 values with dtype=float32]

  • u
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    east-west velocity

    mip_name :

    ua

    mip_units :

    m s-1

    [4400 values with dtype=float32]

  • v
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    north-south velocity

    mip_name :

    va

    mip_units :

    m s-1

    [4400 values with dtype=float32]

  • w
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    vertical velocity

    [4400 values with dtype=float32]

  • ze
    (time, p, lat, lon)
    float32
    ...

    units :

    m

    long_name :

    lower edge altitude (above MSL)

    mip_name :

    zhalf

    [4400 values with dtype=float32]

  • omega
    (time, p, lat, lon)
    float32
    ...

    units :

    hPa/s

    long_name :

    pressure vertical velocity +ve downward

    mip_name :

    wap

    mip_units :

    Pa s-1

    [4400 values with dtype=float32]

  • t
    (time, p, lat, lon)
    float32
    ...

    units :

    K

    long_name :

    temperature

    mip_name :

    ta

    [4400 values with dtype=float32]

  • th
    (time, p, lat, lon)
    float32
    ...

    units :

    K

    long_name :

    potential temperature

    [4400 values with dtype=float32]

  • q
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    specific humidity

    mip_name :

    hus

    mip_units :

    1

    [4400 values with dtype=float32]

  • rhw
    (time, p, lat, lon)
    float32
    ...

    units :

    %

    long_name :

    relative humidity (water)

    [4400 values with dtype=float32]

  • rhi
    (time, p, lat, lon)
    float32
    ...

    units :

    %

    long_name :

    relative humidity (ice)

    [4400 values with dtype=float32]

  • z
    (time, p, lat, lon)
    float32
    ...

    units :

    m

    long_name :

    layer altitude (above MSL)

    mip_name :

    zg

    [4400 values with dtype=float32]

  • p_3d
    (time, p, lat, lon)
    float32
    ...

    units :

    mb

    long_name :

    pressure on model levels

    mip_name :

    pfull

    mip_units :

    Pa

    [4400 values with dtype=float32]

  • is_cnv
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    layer is in turbulent region

    [4400 values with dtype=float32]

  • olr
    (time, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    OUTGOING LW RADIATION at TOA (in RADIA)

    mip_name :

    rlut

    mip_units :

    W m-2

    [40 values with dtype=float32]

  • lwds
    (time, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    LONGWAVE DOWNWARD FLUX at SURFACE

    mip_name :

    rlds

    mip_units :

    W m-2

    [40 values with dtype=float32]

  • lwus
    (time, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    LONGWAVE UPWARD FLUX at SURFACE

    mip_name :

    rlus

    mip_units :

    W m-2

    [40 values with dtype=float32]

  • lwdp
    (time, p, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    Longwave downward flux profile

    mip_name :

    rld

    mip_units :

    W m-2

    [4400 values with dtype=float32]

  • lwup
    (time, p, lat, lon)
    float32
    ...

    units :

    W/m^2

    long_name :

    Longwave upward flux profile

    mip_name :

    rlu

    mip_units :

    W m-2

    [4400 values with dtype=float32]

  • dth_lw
    (time, p, lat, lon)
    float32
    ...

    units :

    K/day

    long_name :

    theta tendency from longwave radiative heating

    [4400 values with dtype=float32]

  • e_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    m2/s2

    long_name :

    atmospheric turbulent kinetic energy

    [4400 values with dtype=float32]

  • w2_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    m2/s2

    long_name :

    twice vertical component of atmospheric tke

    [4400 values with dtype=float32]

  • dissip_tke_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    m2/s3

    long_name :

    atmospheric turbulent kinetic energy dissipation

    [4400 values with dtype=float32]

  • dq_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg/day

    long_name :

    moisture tendency from surface fluxes and turbulence

    mip_name :

    tnhuspbl

    mip_units :

    s-1

    [4400 values with dtype=float32]

  • dth_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    K/day

    long_name :

    theta tendency from surface fluxes and turbulence

    [4400 values with dtype=float32]

  • wq_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg m/s

    long_name :

    atmospheric turbulent water vapor flux

    [4400 values with dtype=float32]

  • uw_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    m2/s2

    long_name :

    turbulent momentum flux x-component

    [4400 values with dtype=float32]

  • vw_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    m2/s2

    long_name :

    turbulent momentum flux y-component

    [4400 values with dtype=float32]

  • wql_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg m/s

    long_name :

    turbulent flux of liquid water mixing ratio

    [4400 values with dtype=float32]

  • wqi_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg m/s

    long_name :

    turbulent flux of ice water mixing ratio

    [4400 values with dtype=float32]

  • wth_turb
    (time, p, lat, lon)
    float32
    ...

    units :

    K m/s

    long_name :

    atmospheric turbulent potential temperature flux

    [4400 values with dtype=float32]

  • lwp
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Liquid Water Path

    [40 values with dtype=float32]

  • iwp
    (time, lat, lon)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    units :

    g/m^2

    long_name :

    Ice Water Path

    mip_name :

    clivi

    mip_units :

    kg m-2

    array([[[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]],
    
           [[0.]]], dtype=float32)

  • snowfall
    (time, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Surface Snowfall Rate

    mip_name :

    prsn

    mip_units :

    kg m-2 s-1

    [40 values with dtype=float32]

  • cLWPss
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Stratiform Cloud Liquid Water Path

    [40 values with dtype=float32]

  • pLWPss
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Stratiform Rain Liquid Water Path

    [40 values with dtype=float32]

  • cIWPss
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Stratiform Cloud Ice Water Path

    [40 values with dtype=float32]

  • pIWPss
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Stratiform Snow Ice Water Path

    [40 values with dtype=float32]

  • cLWPmc
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Convective Cloud Liquid Water Path

    [40 values with dtype=float32]

  • pLWPmc
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Convective Rain Liquid Water Path

    [40 values with dtype=float32]

  • cIWPmc
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Convective Cloud Ice Water Path

    [40 values with dtype=float32]

  • pIWPmc
    (time, lat, lon)
    float32
    ...

    units :

    g/m^2

    long_name :

    Convective Snow Ice Water Path

    [40 values with dtype=float32]

  • prec
    (time, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Surface Precipitation Rate

    mip_name :

    pr

    mip_units :

    kg m-2 s-1

    [40 values with dtype=float32]

  • tau_ss
    (time, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Stratiform cloud(+precip) optical thickness (in-cloud)

    [40 values with dtype=float32]

  • tau_mc
    (time, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective cloud(+precip) optical thickness (in-cloud)

    [40 values with dtype=float32]

  • cldtot_2d
    (time, lat, lon)
    float32
    ...

    units :

    %

    long_name :

    Total Cloud Cover

    [40 values with dtype=float32]

  • cldmc_2d
    (time, lat, lon)
    float32
    ...

    units :

    %

    long_name :

    Convective Cloud Cover

    mip_name :

    cltc

    [40 values with dtype=float32]

  • qcl
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Stratiform Cloud Water

    [4400 values with dtype=float32]

  • qci
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Stratiform Cloud Ice

    [4400 values with dtype=float32]

  • qpl
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Stratiform Rainwater

    [4400 values with dtype=float32]

  • qpi
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Stratiform Snow

    [4400 values with dtype=float32]

  • QCLmc
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Convective Cloud Water

    [4400 values with dtype=float32]

  • QCImc
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Convective Cloud Ice

    [4400 values with dtype=float32]

  • QPLmc
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Convective Rainwater

    [4400 values with dtype=float32]

  • QPImc
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg

    long_name :

    Convective Snow

    [4400 values with dtype=float32]

  • ncl
    (time, p, lat, lon)
    float32
    ...

    units :

    cm-3

    long_name :

    Stratiform Cloud Droplets

    [4400 values with dtype=float32]

  • npl
    (time, p, lat, lon)
    float32
    ...

    units :

    cm-3

    long_name :

    Stratiform Raindrops

    [4400 values with dtype=float32]

  • nci
    (time, p, lat, lon)
    float32
    ...

    units :

    cm-3

    long_name :

    Stratiform Cloud Ice Crystals

    [4400 values with dtype=float32]

  • npi
    (time, p, lat, lon)
    float32
    ...

    units :

    cm-3

    long_name :

    Stratiform Snow Crystals

    [4400 values with dtype=float32]

  • re_mccl
    (time, p, lat, lon)
    float32
    ...

    units :

    micron

    long_name :

    Convective Cloud Droplet Effective Radius

    mip_name :

    reffclwc

    mip_units :

    m

    [4400 values with dtype=float32]

  • re_mcci
    (time, p, lat, lon)
    float32
    ...

    units :

    micron

    long_name :

    Convective Cloud Ice Effective Radius

    mip_name :

    reffclic

    mip_units :

    m

    [4400 values with dtype=float32]

  • re_mcpl
    (time, p, lat, lon)
    float32
    ...

    units :

    micron

    long_name :

    Convective Raindrop Effective Radius

    mip_name :

    reffrainc

    mip_units :

    m

    [4400 values with dtype=float32]

  • re_mcpi
    (time, p, lat, lon)
    float32
    ...

    units :

    micron

    long_name :

    Convective Precipitating Ice Effective Radius

    mip_name :

    reffsnowc

    mip_units :

    m

    [4400 values with dtype=float32]

  • cldsscl
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Stratiform Water Cloud Fraction

    [4400 values with dtype=float32]

  • cldssci
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Stratiform Ice Cloud Fraction

    [4400 values with dtype=float32]

  • cldsspl
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Stratiform Rain Fraction

    [4400 values with dtype=float32]

  • cldsspi
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Stratiform Snow Fraction

    [4400 values with dtype=float32]

  • cldmccl
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective Cloud Water Fraction

    [4400 values with dtype=float32]

  • cldmcpl
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective Rain Fraction

    [4400 values with dtype=float32]

  • cldmcci
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective Cloud Ice Fraction

    [4400 values with dtype=float32]

  • cldmcpi
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective Snow Fraction

    [4400 values with dtype=float32]

  • rain_mc
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Convective rainfall flux

    mip_name :

    prcprof

    mip_units :

    kg m-2 s-1

    [4400 values with dtype=float32]

  • snow_mc
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Convective snowfall flux

    mip_name :

    prsnc

    mip_units :

    kg m-2 s-1

    [4400 values with dtype=float32]

  • ssp_cl_3d
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Stratiform Cloud Water Precipitation Rate

    [4400 values with dtype=float32]

  • ssp_pl_3d
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Stratiform Rainwater Precipitation Rate

    [4400 values with dtype=float32]

  • ssp_ci_3d
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Stratiform Cloud Ice Precipitation Rate

    [4400 values with dtype=float32]

  • ssp_pi_3d
    (time, p, lat, lon)
    float32
    ...

    units :

    mm/d

    long_name :

    Stratiform Snow Precipitation Rate

    [4400 values with dtype=float32]

  • cfr
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Cloud fraction (includes precip seen by radiation)

    mip_name :

    cl

    mip_units :

    %

    [4400 values with dtype=float32]

  • cldmcr
    (time, p, lat, lon)
    float32
    ...

    units :

    -

    long_name :

    Convective Cloud Fraction (includes precip seen by radiation)

    mip_name :

    clc

    mip_units :

    %

    [4400 values with dtype=float32]

  • dq_mc
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg/day

    long_name :

    Moisture Tendency from Moist Convection

    mip_name :

    tnhusc

    mip_units :

    s-1

    [4400 values with dtype=float32]

  • dth_mc
    (time, p, lat, lon)
    float32
    ...

    units :

    K/day

    long_name :

    Theta Tendency from Moist Convection

    [4400 values with dtype=float32]

  • dq_ss
    (time, p, lat, lon)
    float32
    ...

    units :

    kg/kg/day

    long_name :

    Moisture Tendency from Stratiform Cloud

    mip_name :

    tnhusscp

    mip_units :

    s-1

    [4400 values with dtype=float32]

  • dth_ss
    (time, p, lat, lon)
    float32
    ...

    units :

    K/day

    long_name :

    Theta Tendency from Stratiform Cloud

    [4400 values with dtype=float32]

  • vm_sscl
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Stratiform Cloud Water Fall Speed

    [4400 values with dtype=float32]

  • vm_sspl
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Stratiform Rain Fall Speed

    [4400 values with dtype=float32]

  • vm_ssci
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Stratiform Cloud Ice Fall Speed

    [4400 values with dtype=float32]

  • vm_sspi
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Stratiform Snow Fall Speed

    [4400 values with dtype=float32]

  • vm_mccl
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Convective Cloud Liquid Fall Speed

    [4400 values with dtype=float32]

  • vm_mcpl
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Convective Precipitating Liquid Fall Speed

    [4400 values with dtype=float32]

  • vm_mcci
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Convective Cloud Ice Fall Speed

    [4400 values with dtype=float32]

  • vm_mcpi
    (time, p, lat, lon)
    float32
    ...

    units :

    m/s

    long_name :

    Mass-Weighted Convective Precipitating Ice Fall Speed

    [4400 values with dtype=float32]

• Indexes: (4)
  • lon
    PandasIndex

    PandasIndex(Float64Index([10.0], dtype='float64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Float64Index([74.5], dtype='float64', name='lat'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2020-03-12 22:15:00, 2020-03-12 22:45:00, 2020-03-12 23:15:00,
                 2020-03-12 23:45:00, 2020-03-13 00:15:00, 2020-03-13 00:45:00,
                 2020-03-13 01:15:00, 2020-03-13 01:45:00, 2020-03-13 02:15:00,
                 2020-03-13 02:45:00, 2020-03-13 03:15:00, 2020-03-13 03:45:00,
                 2020-03-13 04:15:00, 2020-03-13 04:45:00, 2020-03-13 05:15:00,
                 2020-03-13 05:45:00, 2020-03-13 06:15:00, 2020-03-13 06:45:00,
                 2020-03-13 07:15:00, 2020-03-13 07:45:00, 2020-03-13 08:15:00,
                 2020-03-13 08:45:00, 2020-03-13 09:15:00, 2020-03-13 09:45:00,
                 2020-03-13 10:15:00, 2020-03-13 10:45:00, 2020-03-13 11:15:00,
                 2020-03-13 11:45:00, 2020-03-13 12:15:00, 2020-03-13 12:45:00,
                 2020-03-13 13:15:00, 2020-03-13 13:45:00, 2020-03-13 14:15:00,
                 2020-03-13 14:45:00, 2020-03-13 15:15:00, 2020-03-13 15:45:00,
                 2020-03-13 16:15:00, 2020-03-13 16:45:00, 2020-03-13 17:15:00,
                 2020-03-13 17:45:00],
                dtype='object', length=40, calendar='noleap', freq='30min'))

  • p
    PandasIndex

    PandasIndex(Float64Index([                979.0,                 969.0,
                                  959.0,                 949.0,
                                  939.0,                 929.0,
                                  919.0,                 909.0,
                                  899.0,                 888.5,
                  ...
                     0.5630000233650208,    0.3959999978542328,
                    0.24699999392032623,   0.13899999856948853,
                    0.07800000160932541,   0.04399999976158142,
                    0.02500000037252903,  0.014000000432133675,
                   0.007499999832361937, 0.0035000001080334187],
                 dtype='float64', name='p', length=110))

• Attributes: (1)

  xlabel :

  SCM_COMBLE-MIP_t705ml_noice COMBLE-MIP Ent PPE (CAO case using Single Column Model)

Calculate and append additional variables

dummy_sca = modele_data['lwp']*0.
modele_data = modele_data.assign(clwpt = dummy_sca + modele_data['cLWPss'].data + modele_data['cLWPmc'].data)
modele_data = modele_data.assign(rlwpt = dummy_sca + modele_data['pLWPss'].data + modele_data['pLWPmc'].data)
if do_ice: modele_data = modele_data.assign(iwpt = dummy_sca + modele_data['cIWPss'].data + modele_data['cIWPmc'].data + 
                                            modele_data['pIWPss'].data + modele_data['pIWPmc'].data)
modele_data = modele_data.assign(tau = dummy_sca + modele_data['tau_ss'].data + modele_data['tau_mc'].data)

dummy_snd = modele_data['q']*0.
modele_data = modele_data.assign(rhobar = dummy_snd + 100.*modele_data['p_3d'].data/(287.05*modele_data['t'].data))
modele_data = modele_data.assign(qlc = dummy_snd + modele_data['qcl'].data + modele_data['QCLmc'].data)
modele_data = modele_data.assign(qlr = dummy_snd + modele_data['qpl'].data + modele_data['QPLmc'].data)
if do_ice: modele_data = modele_data.assign(qi = dummy_snd + modele_data['qci'].data + modele_data['qpi'].data + 
                                            modele_data['QCImc'].data + modele_data['QPImc'].data)
modele_data = modele_data.assign(lcf = dummy_snd + modele_data['cldsscl'].data + modele_data['cldmccl'].data)
modele_data['lcf'].values = np.clip(modele_data['lcf'].values,0.,1.)
modele_data = modele_data.assign(prt = dummy_snd + modele_data['ssp_cl_3d'].data + modele_data['ssp_pl_3d'].data + modele_data['rain_mc'].data)
if do_ice: 
    modele_data = modele_data.assign(pit = dummy_snd + modele_data['ssp_ci_3d'].data + modele_data['ssp_pi_3d'].data + modele_data['snow_mc'].data)
    modele_data['prt'].data += modele_data['pit'].data
modele_data = modele_data.assign(dth_micro = dummy_snd + modele_data['dth_ss'].data + modele_data['dth_mc'].data)
modele_data = modele_data.assign(dq_micro = dummy_snd + modele_data['dq_ss'].data + modele_data['dq_mc'].data)
modele_data = modele_data.assign(wqt_turb = dummy_snd + modele_data['wq_turb'].data + modele_data['wql_turb'].data + modele_data['wqi_turb'].data)

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 ModelE3 variables to requested outputs

Expand the table to include columns that indicate ModelE3 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 ModelE3 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] = 'p_3d'
#    if standard_name=='layer_top_pressure': 
#        vars_mean_list.model_name.iat[index] = 'pe_t'
    if standard_name=='surface_pressure': 
        vars_mean_list.model_name.iat[index] = 'prsurf'
        vars_mean_list.conv_factor.iat[index] = 100.
    if standard_name=='surface_temperature': 
        vars_mean_list.model_name.iat[index] = 'gtempr'
    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] = 'shflx'
        vars_mean_list.conv_factor.iat[index] = -1.
    if standard_name=='surface_upward_latent_heat_flux': 
        vars_mean_list.model_name.iat[index] = 'lhflx'
        vars_mean_list.conv_factor.iat[index] = -1.
    if standard_name=='obukhov_length': 
        vars_mean_list.model_name.iat[index] = 'lmonin'
    if standard_name=='pbl_height': 
        vars_mean_list.model_name.iat[index] = 'pblht_bp'
    if standard_name=='inversion_height': 
        vars_mean_list.model_name.iat[index] = 'pblht_th'
    if standard_name=='atmosphere_mass_content_of_liquid_cloud_water': 
        vars_mean_list.model_name.iat[index] = 'clwpt'
        vars_mean_list.conv_factor.iat[index] = 0.001
    if standard_name=='atmosphere_mass_content_of_rain_water': 
        vars_mean_list.model_name.iat[index] = 'rlwpt'
        vars_mean_list.conv_factor.iat[index] = 0.001
    if do_ice:
        if standard_name=='atmosphere_mass_content_of_ice_water': 
            vars_mean_list.model_name.iat[index] = 'iwpt'
            vars_mean_list.conv_factor.iat[index] = 0.001
    if standard_name=='area_fraction_cover_of_hydrometeors': 
        vars_mean_list.model_name.iat[index] = 'cldtot_2d'
#    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] = 'cldmc_2d'
    if standard_name=='optical_depth': 
        vars_mean_list.model_name.iat[index] = 'tau'
#    if standard_name=='optical_depth_of_liquid_water': 
#        vars_mean_list.model_name.iat[index] = ''
    if standard_name=='precipitation_flux_at_surface': 
        vars_mean_list.model_name.iat[index] = 'prec'
        vars_mean_list.conv_factor.iat[index] = 1./86400
    if standard_name=='precipitation_flux_of_ice_at_surface': 
        vars_mean_list.model_name.iat[index] = 'snowfall'
        vars_mean_list.conv_factor.iat[index] = 1./86400
    if standard_name=='toa_outgoing_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'olr'
    if standard_name=='surface_downwelling_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'lwds'  
    if standard_name=='surface_upwelling_longwave_flux': 
        vars_mean_list.model_name.iat[index] = 'lwus'  
    if standard_name=='height': 
        vars_mean_list.model_name.iat[index] = 'z'
    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] = 'q'
    if standard_name=='relative_humidity': 
        vars_mean_list.model_name.iat[index] = 'rhw'
        vars_mean_list.conv_factor.iat[index] = 0.01
    if standard_name=='relative_humidity_over_ice': 
        vars_mean_list.model_name.iat[index] = 'rhi'
        vars_mean_list.conv_factor.iat[index] = 0.01
    if standard_name=='air_potential_temperature': 
        vars_mean_list.model_name.iat[index] = 'th'
    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] = 'cfr'
    if standard_name=='area_fraction_of_liquid_cloud': 
        vars_mean_list.model_name.iat[index] = 'lcf'
    if standard_name=='area_fraction_of_convective_hydrometeors': 
        vars_mean_list.model_name.iat[index] = 'cldmcr'
    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] = 'e_turb'
    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_turb'
    if standard_name=='meridional_momentum_flux': 
        vars_mean_list.model_name.iat[index] = 'vw_turb'
    if standard_name=='variance_of_upward_air_velocity': 
        vars_mean_list.model_name.iat[index] = 'w2_turb'
    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] = 'lwdp'
    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] = '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] = 'qcl'
    if standard_name=='mass_mixing_ratio_of_rain_water_in_air_stratiform': 
        vars_mean_list.model_name.iat[index] = 'qpl'
    if do_ice:
        if standard_name=='mass_mixing_ratio_of_ice_cloud_in_air_stratiform': 
            vars_mean_list.model_name.iat[index] = 'qci'
        if standard_name=='mass_mixing_ratio_of_ice_precipitation_in_air_stratiform': 
            vars_mean_list.model_name.iat[index] = 'qpi'
    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] = 'ncl'
    if standard_name=='number_of_rain_drops_in_air_stratiform': 
        vars_mean_list.model_name.iat[index] = 'npl'
    if do_ice:
        if standard_name=='number_of_ice_cloud_crystals_in_air_stratiform': 
            vars_mean_list.model_name.iat[index] = 'nci'
        if standard_name=='number_of_ice_precipitation_crystals_in_air_stratiform': 
            vars_mean_list.model_name.iat[index] = 'npi'
    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] = 'cldsscl'
    if standard_name=='area_fraction_of_rain_stratiform': 
        vars_mean_list.model_name.iat[index] = 'cldsspl'
    if do_ice:
        if standard_name=='area_fraction_of_ice_cloud_stratiform': 
            vars_mean_list.model_name.iat[index] = 'cldssci'
        if standard_name=='area_fraction_of_ice_precipitation_stratiform': 
            vars_mean_list.model_name.iat[index] = 'cldsspi'
    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'
    if standard_name=='mass_weighted_fall_speed_of_liquid_cloud_water_stratiform': 
        vars_mean_list.model_name.iat[index] = 'vm_sscl'
    if standard_name=='mass_weighted_fall_speed_of_rain_stratiform': 
        vars_mean_list.model_name.iat[index] = 'vm_sspl'
    if do_ice:
        if standard_name=='mass_weighted_fall_speed_of_ice_cloud_stratiform': 
            vars_mean_list.model_name.iat[index] = 'vm_ssci'
        if standard_name=='mass_weighted_fall_speed_of_ice_precipitation_stratiform': 
            vars_mean_list.model_name.iat[index] = 'vm_sspi'
    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[2:] # echo variables (first two rows are dimensions)

	standard_name	variable_id	units	dimensions	model_name	conv_factor
2	air_pressure	pa	Pa	time, layer	p_3d	1.000000
3	layer_top_pressure	pe	Pa	time, layer	missing data	1.000000
4	surface_pressure	ps	Pa	time	prsurf	100.000000
5	surface_temperature	ts	K	time	gtempr	1.000000
6	surface_friction_velocity	ustar	m s-1	time	ustar	1.000000
7	surface_roughness_length_for_momentum_in_air	z0	m	time	missing data	1.000000
8	surface_roughness_length_for_heat_in_air	z0h	m	time	missing data	1.000000
9	surface_roughness_length_for_humidity_in_air	z0q	m	time	missing data	1.000000
10	surface_upward_sensible_heat_flux	hfss	W m-2	time	shflx	-1.000000
11	surface_upward_latent_heat_flux	hfls	W m-2	time	lhflx	-1.000000
12	obukhov_length	ol	m	time	lmonin	1.000000
13	pbl_height	pblh	m	time	pblht_bp	1.000000
14	inversion_height	zi	m	time	pblht_th	1.000000
15	atmosphere_mass_content_of_liquid_cloud_water	lwpc	kg m-2	time	clwpt	0.001000
16	atmosphere_mass_content_of_rain_water	lwpr	kg m-2	time	rlwpt	0.001000
17	atmosphere_mass_content_of_ice_water	iwp	kg m-2	time	missing data	1.000000
18	area_fraction_cover_of_hydrometeors	cf	1	time	cldtot_2d	1.000000
19	area_fraction_cover_of_liquid_cloud	cflc	1	time	missing data	1.000000
20	area_fraction_cover_of_convective_hydrometeors	cfc	1	time	cldmc_2d	1.000000
21	optical_depth	od	1	time	tau	1.000000
22	optical_depth_of_liquid_cloud	odlc	1	time	missing data	1.000000
23	precipitation_flux_at_surface	pr	kg m-2 s-1	time	prec	0.000012
24	precipitation_flux_of_ice_at_surface	pri	kg m-2 s-1	time	snowfall	0.000012
25	toa_outgoing_longwave_flux	rlut	W m-2	time	olr	1.000000
26	surface_downwelling_longwave_flux	rlds	W m-2	time	lwds	1.000000
27	surface_upwelling_longwave_flux	rlus	W m-2	time	lwus	1.000000
28	surface_sea_spray_number_flux	ssaf	m-2 s-1	time	missing data	1.000000
29	height	zf	m	time, layer	z	1.000000
30	eastward_wind	ua	m s-1	time, layer	u	1.000000
31	northward_wind	va	m s-1	time, layer	v	1.000000
32	air_dry_density	rhoa	kg m-3	time, layer	rhobar	1.000000
33	air_temperature	ta	K	time, layer	t	1.000000
34	water_vapor_mixing_ratio	qv	kg kg-1	time, layer	q	1.000000
35	relative_humidity	hur	1	time, layer	rhw	0.010000
36	relative_humidity_over_ice	huri	1	time, layer	rhi	0.010000
37	air_potential_temperature	theta	K	time, layer	th	1.000000
38	mass_mixing_ratio_of_cloud_liquid_water_in_air	qlc	kg kg-1	time, layer	qlc	1.000000
39	mass_mixing_ratio_of_rain_water_in_air	qlr	kg kg-1	time, layer	qlr	1.000000
40	mass_mixing_ratio_of_ice_water_in_air	qi	kg kg-1	time, layer	missing data	1.000000
41	area_fraction_of_hydrometeors	fh	1	time, layer	cfr	1.000000
42	area_fraction_of_liquid_cloud	flc	1	time, layer	lcf	1.000000
43	area_fraction_of_convective_hydrometeors	fc	1	time, layer	cldmcr	1.000000
44	precipitation_flux_in_air	prf	kg m-2 s-1	time, layer	prt	0.000012
45	precipitation_flux_in_air_in_ice_phase	prfi	kg m-2 s-1	time, layer	missing data	1.000000
46	specific_turbulent_kinetic_energy	tke	m2 s-2	time, layer	e_turb	1.000000
47	dissipation_rate_of_turbulent_kinetic_energy	eps	m2 s-3	time, layer	missing data	1.000000
48	zonal_momentum_flux	uw	m2 s-2	time, layer	uw_turb	1.000000
49	meridional_momentum_flux	vw	m2 s-2	time, layer	vw_turb	1.000000
50	variance_of_upward_air_velocity	w2	m2 s-2	time, layer	w2_turb	1.000000
51	vertical_flux_potential_temperature	wth	K m s-1	time, layer	wth_turb	1.000000
52	vertical_flux_liquid_ice_water_potential_tempe...	vf_thli	K m s-1	time, layer	missing data	1.000000
53	vertical_flux_water_vapor	wqv	kg kg-1 m s-1	time, layer	wq_turb	1.000000
54	vertical_flux_total_water	vf_qt	kg kg-1 m s-1	time, layer	wqt_turb	1.000000
55	convection_updraft_mass_flux	cmfu	kg m-2 s-1	time, layer	lwdp	1.000000
56	convection_downdraft_mass_flux	cmfd	kg m-2 s-1	time, layer	lwdp	1.000000
57	downwelling_longwave_flux_in_air	rld	W m-2	time, layer	lwdp	1.000000
58	upwelling_longwave_flux_in_air	rlu	W m-2	time, layer	lwup	1.000000
59	tendency_of_air_potential_temperature_due_to_r...	dth_rad	K s-1	time, layer	dth_lw	0.000012
60	tendency_of_air_potential_temperature_due_to_m...	dth_micro	K s-1	time, layer	dth_micro	0.000012
61	tendency_of_air_potential_temperature_due_to_m...	dth_turb	K s-1	time, layer	dth_turb	0.000012
62	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_micro	s-1	time, layer	dq_micro	0.000012
63	tendency_of_water_vapor_mixing_ratio_due_to_mi...	dq_turb	s-1	time, layer	dq_turb	0.000012
64	number_of_total_aerosol_mode1	na1	kg-1	time, layer	missing data	1.000000
65	number_of_total_aerosol_mode2	na2	kg-1	time, layer	missing data	1.000000
66	number_of_total_aerosol_mode3	na3	kg-1	time, layer	missing data	1.000000
67	tendency_of_aerosol_number_due_to_warm_microph...	dna_micro_warm	kg-1 s-1	time, layer	missing data	1.000000
68	tendency_of_aerosol_number_due_to_cold_microph...	dna_micro_cold	kg-1 s-1	time, layer	missing data	1.000000
69	tendency_of_aerosol_number_due_to_mixing	dna_turb	kg-1 s-1	time, layer	missing data	1.000000
70	tendency_of_ice_number_due_to_heterogeneous_fr...	dni_het	kg-1 s-1	time, layer	missing data	1.000000
71	tendency_of_ice_number_due_to_secondary_ice_pr...	dni_sip	kg-1 s-1	time, layer	missing data	1.000000
72	tendency_of_ice_number_due_to_homogeneous_free...	dni_hom	kg-1 s-1	time, layer	missing data	1.000000
73	mass_mixing_ratio_of_liquid_cloud_water_in_air...	qlcs	kg kg-1	time, layer	qcl	1.000000
74	mass_mixing_ratio_of_rain_water_in_air_stratiform	qlrs	kg kg-1	time, layer	qpl	1.000000
75	mass_mixing_ratio_of_ice_cloud_in_air_stratiform	qics	kg kg-1	time, layer	missing data	1.000000
76	mass_mixing_ratio_of_ice_precipitation_in_air_...	qips	kg kg-1	time, layer	missing data	1.000000
77	mass_mixing_ratio_of_liquid_cloud_water_in_air...	qlcc	kg kg-1	time, layer	QCLmc	1.000000
78	mass_mixing_ratio_of_rain_water_in_air_convective	qlrc	kg kg-1	time, layer	QPLmc	1.000000
79	mass_mixing_ratio_of_ice_cloud_in_air_convective	qicc	kg kg-1	time, layer	missing data	1.000000
80	mass_mixing_ratio_of_ice_precipitation_in_air_...	qipc	kg kg-1	time, layer	missing data	1.000000
81	number_of_liquid_cloud_droplets_in_air_stratiform	nlcs	kg-1	time, layer	ncl	1.000000
82	number_of_rain_drops_in_air_stratiform	nlrs	kg-1	time, layer	npl	1.000000
83	number_of_ice_cloud_crystals_in_air_stratiform	nics	kg-1	time, layer	missing data	1.000000
84	number_of_ice_precipitation_crystals_in_air_st...	nips	kg-1	time, layer	missing data	1.000000
85	effective_radius_of_liquid_cloud_droplets_conv...	relcc	m	time, layer	re_mccl	1.000000
86	effective_radius_of_rain_convective	relrc	m	time, layer	re_mcpl	1.000000
87	effective_radius_of_ice_cloud_convective	reicc	m	time, layer	missing data	1.000000
88	effective_radius_of_ice_precipitation_convective	reipc	m	time, layer	missing data	1.000000
89	area_fraction_of_liquid_cloud_stratiform	flcs	1	time, layer	cldsscl	1.000000
90	area_fraction_of_rain_stratiform	flrs	1	time, layer	cldsspl	1.000000
91	area_fraction_of_ice_cloud_stratiform	fics	1	time, layer	missing data	1.000000
92	area_fraction_of_ice_precipitation_stratiform	fips	1	time, layer	missing data	1.000000
93	area_fraction_of_liquid_cloud_convective	flcc	1	time, layer	cldmccl	1.000000
94	area_fraction_of_rain_convective	flrc	1	time, layer	cldmcpl	1.000000
95	area_fraction_of_ice_cloud_convective	ficc	1	time, layer	missing data	1.000000
96	area_fraction_of_ice_precipitation_convective	fipc	1	time, layer	missing data	1.000000
97	mass_weighted_fall_speed_of_liquid_cloud_water...	vmlcs	m s-1	time, layer	vm_sscl	1.000000
98	mass_weighted_fall_speed_of_rain_stratiform	vmlrs	m s-1	time, layer	vm_sspl	1.000000
99	mass_weighted_fall_speed_of_ice_cloud_stratiform	vmics	m s-1	time, layer	missing data	1.000000
100	mass_weighted_fall_speed_of_ice_precipitation_...	vmips	m s-1	time, layer	missing data	1.000000
101	mass_weighted_fall_speed_of_liquid_cloud_water...	vmlcc	m s-1	time, layer	vm_mccl	1.000000
102	mass_weighted_fall_speed_of_rain_convective	vmlrc	m s-1	time, layer	vm_mcpl	1.000000
103	mass_weighted_fall_speed_of_cloud_ice_crystals...	vmicc	m s-1	time, layer	missing data	1.000000
104	mass_weighted_fall_speed_of_ice_precipitation_...	vmipc	m s-1	time, layer	missing data	1.000000

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='ModelE3 SCM results for COMBLE-MIP case: fixed stratiform Nd and Ni'
dephy_file.reference='https://github.com/ARM-Development/comble-mip'
dephy_file.authors='Ann Fridlind (ann.fridlind@nasa.gov), Florian Tornow (florian.tornow@nasa.gov), Andrew Ackerman (andrew.ackerman@nasa.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_ModelE3_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=modele_data.sizes['p']

# create dimensions
nt = modele_data.sizes['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(modele_data['time'].data[0]),'%Y-%m-%d %H:%M:%S')
time2 = dt.datetime.strptime(str(modele_data['time'].data[1]),'%Y-%m-%d %H:%M:%S')
delta_t = (time2-time1).total_seconds()
time[:] = (np.arange(nt)+1.)*delta_t

nl = modele_data.sizes['p']
layer = dephy_file.createDimension('layer', nl)
layer = dephy_file.createVariable('layer', np.float64, ('layer',))
layer.units = '1'
layer.long_name = 'pressure_layer'
layer[:] = np.arange(nl)+1

# 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[:] = modele_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[:] = modele_data[mod_name].data*c_factor

print(dephy_file)
dephy_file.close()

The file ./../../output_scm/modelE/sandbox/ModelE3-Tun2_FixN_noice_def_z0.nc has been deleted successfully
<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_CLASSIC data model, file format NETCDF3):
    title: ModelE3 SCM results for COMBLE-MIP case: fixed stratiform Nd and Ni
    reference: https://github.com/ARM-Development/comble-mip
    authors: Ann Fridlind (ann.fridlind@nasa.gov), Florian Tornow (florian.tornow@nasa.gov), Andrew Ackerman (andrew.ackerman@nasa.gov)
    source: /data/home/fridlind/modelE3//allsteps.allmergeSCM_COMBLE-MIP_t705ml_noice.nc
    version: 2024-05-21 23:04:13
    format_version: DEPHY SCM format version 1.6
    script: convert_ModelE3_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: 110
    dimensions(sizes): time(40), layer(110)
    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)
dephy_check

<xarray.Dataset> Size: 3MB
Dimensions:         (time: 40, layer: 110)
Coordinates:
  * time            (time) datetime64[ns] 320B 2020-03-12T22:30:00 ... 2020-0...
  * layer           (layer) float64 880B 1.0 2.0 3.0 4.0 ... 108.0 109.0 110.0
Data variables: (12/103)
    pa              (time, layer) float64 35kB ...
    pe              (time, layer) float64 35kB ...
    ps              (time) float64 320B ...
    ts              (time) float64 320B ...
    ustar           (time) float64 320B ...
    z0              (time) float64 320B ...
    ...              ...
    vmics           (time, layer) float64 35kB ...
    vmips           (time, layer) float64 35kB ...
    vmlcc           (time, layer) float64 35kB ...
    vmlrc           (time, layer) float64 35kB ...
    vmicc           (time, layer) float64 35kB ...
    vmipc           (time, layer) float64 35kB ...
Attributes: (12/14)
    title:           ModelE3 SCM results for COMBLE-MIP case: fixed stratifor...
    reference:       https://github.com/ARM-Development/comble-mip
    authors:         Ann Fridlind (ann.fridlind@nasa.gov), Florian Tornow (fl...
    source:          /data/home/fridlind/modelE3//allsteps.allmergeSCM_COMBLE...
    version:         2024-05-21 23:04:13
    format_version:  DEPHY SCM format version 1.6
    ...              ...
    force_geo:       1
    surfaceType:     ocean
    surfaceForcing:  ts
    lat:             74.5 deg N
    dp:              see pressure variable
    np:              110

xarray.Dataset

• Dimensions:
  • time: 40
  • layer: 110
• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2020-03-12T22:30:00 ... 2020-03-...

    long_name :

    time

    array(['2020-03-12T22:30:00.000000000', '2020-03-12T23:00:00.000000000',
           '2020-03-12T23:30:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-13T00:30:00.000000000', '2020-03-13T01:00:00.000000000',
           '2020-03-13T01:30:00.000000000', '2020-03-13T02:00:00.000000000',
           '2020-03-13T02:30:00.000000000', '2020-03-13T03:00:00.000000000',
           '2020-03-13T03:30:00.000000000', '2020-03-13T04:00:00.000000000',
           '2020-03-13T04:30:00.000000000', '2020-03-13T05:00:00.000000000',
           '2020-03-13T05:30:00.000000000', '2020-03-13T06:00:00.000000000',
           '2020-03-13T06:30:00.000000000', '2020-03-13T07:00:00.000000000',
           '2020-03-13T07:30:00.000000000', '2020-03-13T08:00:00.000000000',
           '2020-03-13T08:30:00.000000000', '2020-03-13T09:00:00.000000000',
           '2020-03-13T09:30:00.000000000', '2020-03-13T10:00:00.000000000',
           '2020-03-13T10:30:00.000000000', '2020-03-13T11:00:00.000000000',
           '2020-03-13T11:30:00.000000000', '2020-03-13T12:00:00.000000000',
           '2020-03-13T12:30:00.000000000', '2020-03-13T13:00:00.000000000',
           '2020-03-13T13:30:00.000000000', '2020-03-13T14:00:00.000000000',
           '2020-03-13T14:30:00.000000000', '2020-03-13T15:00:00.000000000',
           '2020-03-13T15:30:00.000000000', '2020-03-13T16:00:00.000000000',
           '2020-03-13T16:30:00.000000000', '2020-03-13T17:00:00.000000000',
           '2020-03-13T17:30:00.000000000', '2020-03-13T18:00:00.000000000'],
          dtype='datetime64[ns]')

  • layer
    (layer)
    float64
    1.0 2.0 3.0 ... 108.0 109.0 110.0

    units :

    1

    long_name :

    pressure_layer

    array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,
            13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,
            25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,
            37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,
            49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,
            61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,
            73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,
            85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,
            97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107., 108.,
           109., 110.])

• Data variables: (103)
  • pa
    (time, layer)
    float64
    ...

    units :

    Pa

    long_name :

    air_pressure

    [4400 values with dtype=float64]

  • pe
    (time, layer)
    float64
    ...

    units :

    Pa

    long_name :

    layer_top_pressure

    [4400 values with dtype=float64]

  • ps
    (time)
    float64
    ...

    units :

    Pa

    long_name :

    surface_pressure

    [40 values with dtype=float64]

  • ts
    (time)
    float64
    ...

    units :

    K

    long_name :

    surface_temperature

    [40 values with dtype=float64]

  • ustar
    (time)
    float64
    ...

    units :

    m s-1

    long_name :

    surface_friction_velocity

    [40 values with dtype=float64]

  • z0
    (time)
    float64
    ...

    units :

    m

    long_name :

    surface_roughness_length_for_momentum_in_air

    [40 values with dtype=float64]

  • z0h
    (time)
    float64
    ...

    units :

    m

    long_name :

    surface_roughness_length_for_heat_in_air

    [40 values with dtype=float64]

  • z0q
    (time)
    float64
    ...

    units :

    m

    long_name :

    surface_roughness_length_for_humidity_in_air

    [40 values with dtype=float64]

  • hfss
    (time)
    float64
    ...

    units :

    W m-2

    long_name :

    surface_upward_sensible_heat_flux

    [40 values with dtype=float64]

  • hfls
    (time)
    float64
    ...

    units :

    W m-2

    long_name :

    surface_upward_latent_heat_flux

    [40 values with dtype=float64]

  • ol
    (time)
    float64
    ...

    units :

    m

    long_name :

    obukhov_length

    [40 values with dtype=float64]

  • pblh
    (time)
    float64
    ...

    units :

    m

    long_name :

    pbl_height

    [40 values with dtype=float64]

  • zi
    (time)
    float64
    ...

    units :

    m

    long_name :

    inversion_height

    [40 values with dtype=float64]

  • lwpc
    (time)
    float64
    ...

    units :

    kg m-2

    long_name :

    atmosphere_mass_content_of_liquid_cloud_water

    [40 values with dtype=float64]

  • lwpr
    (time)
    float64
    ...

    units :

    kg m-2

    long_name :

    atmosphere_mass_content_of_rain_water

    [40 values with dtype=float64]

  • iwp
    (time)
    float64
    ...

    units :

    kg m-2

    long_name :

    atmosphere_mass_content_of_ice_water

    [40 values with dtype=float64]

  • cf
    (time)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_cover_of_hydrometeors

    [40 values with dtype=float64]

  • cflc
    (time)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_cover_of_liquid_cloud

    [40 values with dtype=float64]

  • cfc
    (time)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_cover_of_convective_hydrometeors

    [40 values with dtype=float64]

  • od
    (time)
    float64
    ...

    units :

    1

    long_name :

    optical_depth

    [40 values with dtype=float64]

  • odlc
    (time)
    float64
    ...

    units :

    1

    long_name :

    optical_depth_of_liquid_cloud

    [40 values with dtype=float64]

  • pr
    (time)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    precipitation_flux_at_surface

    [40 values with dtype=float64]

  • pri
    (time)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    precipitation_flux_of_ice_at_surface

    [40 values with dtype=float64]

  • rlut
    (time)
    float64
    ...

    units :

    W m-2

    long_name :

    toa_outgoing_longwave_flux

    [40 values with dtype=float64]

  • rlds
    (time)
    float64
    ...

    units :

    W m-2

    long_name :

    surface_downwelling_longwave_flux

    [40 values with dtype=float64]

  • rlus
    (time)
    float64
    ...

    units :

    W m-2

    long_name :

    surface_upwelling_longwave_flux

    [40 values with dtype=float64]

  • ssaf
    (time)
    float64
    ...

    units :

    m-2 s-1

    long_name :

    surface_sea_spray_number_flux

    [40 values with dtype=float64]

  • zf
    (time, layer)
    float64
    ...

    units :

    m

    long_name :

    height

    [4400 values with dtype=float64]

  • ua
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    eastward_wind

    [4400 values with dtype=float64]

  • va
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    northward_wind

    [4400 values with dtype=float64]

  • rhoa
    (time, layer)
    float64
    ...

    units :

    kg m-3

    long_name :

    air_dry_density

    [4400 values with dtype=float64]

  • ta
    (time, layer)
    float64
    ...

    units :

    K

    long_name :

    air_temperature

    [4400 values with dtype=float64]

  • qv
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    water_vapor_mixing_ratio

    [4400 values with dtype=float64]

  • hur
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    relative_humidity

    [4400 values with dtype=float64]

  • huri
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    relative_humidity_over_ice

    [4400 values with dtype=float64]

  • theta
    (time, layer)
    float64
    ...

    units :

    K

    long_name :

    air_potential_temperature

    [4400 values with dtype=float64]

  • qlc
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_cloud_liquid_water_in_air

    [4400 values with dtype=float64]

  • qlr
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_rain_water_in_air

    [4400 values with dtype=float64]

  • qi
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_ice_water_in_air

    [4400 values with dtype=float64]

  • fh
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_hydrometeors

    [4400 values with dtype=float64]

  • flc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_liquid_cloud

    [4400 values with dtype=float64]

  • fc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_convective_hydrometeors

    [4400 values with dtype=float64]

  • prf
    (time, layer)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    precipitation_flux_in_air

    [4400 values with dtype=float64]

  • prfi
    (time, layer)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    precipitation_flux_in_air_in_ice_phase

    [4400 values with dtype=float64]

  • tke
    (time, layer)
    float64
    ...

    units :

    m2 s-2

    long_name :

    specific_turbulent_kinetic_energy

    [4400 values with dtype=float64]

  • eps
    (time, layer)
    float64
    ...

    units :

    m2 s-3

    long_name :

    dissipation_rate_of_turbulent_kinetic_energy

    [4400 values with dtype=float64]

  • uw
    (time, layer)
    float64
    ...

    units :

    m2 s-2

    long_name :

    zonal_momentum_flux

    [4400 values with dtype=float64]

  • vw
    (time, layer)
    float64
    ...

    units :

    m2 s-2

    long_name :

    meridional_momentum_flux

    [4400 values with dtype=float64]

  • w2
    (time, layer)
    float64
    ...

    units :

    m2 s-2

    long_name :

    variance_of_upward_air_velocity

    [4400 values with dtype=float64]

  • wth
    (time, layer)
    float64
    ...

    units :

    K m s-1

    long_name :

    vertical_flux_potential_temperature

    [4400 values with dtype=float64]

  • vf_thli
    (time, layer)
    float64
    ...

    units :

    K m s-1

    long_name :

    vertical_flux_liquid_ice_water_potential_temperature

    [4400 values with dtype=float64]

  • wqv
    (time, layer)
    float64
    ...

    units :

    kg kg-1 m s-1

    long_name :

    vertical_flux_water_vapor

    [4400 values with dtype=float64]

  • vf_qt
    (time, layer)
    float64
    ...

    units :

    kg kg-1 m s-1

    long_name :

    vertical_flux_total_water

    [4400 values with dtype=float64]

  • cmfu
    (time, layer)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    convection_updraft_mass_flux

    [4400 values with dtype=float64]

  • cmfd
    (time, layer)
    float64
    ...

    units :

    kg m-2 s-1

    long_name :

    convection_downdraft_mass_flux

    [4400 values with dtype=float64]

  • rld
    (time, layer)
    float64
    ...

    units :

    W m-2

    long_name :

    downwelling_longwave_flux_in_air

    [4400 values with dtype=float64]

  • rlu
    (time, layer)
    float64
    ...

    units :

    W m-2

    long_name :

    upwelling_longwave_flux_in_air

    [4400 values with dtype=float64]

  • dth_rad
    (time, layer)
    float64
    ...

    units :

    K s-1

    long_name :

    tendency_of_air_potential_temperature_due_to_radiative_heating

    [4400 values with dtype=float64]

  • dth_micro
    (time, layer)
    float64
    ...

    units :

    K s-1

    long_name :

    tendency_of_air_potential_temperature_due_to_microphysics

    [4400 values with dtype=float64]

  • dth_turb
    (time, layer)
    float64
    ...

    units :

    K s-1

    long_name :

    tendency_of_air_potential_temperature_due_to_mixing

    [4400 values with dtype=float64]

  • dq_micro
    (time, layer)
    float64
    ...

    units :

    s-1

    long_name :

    tendency_of_water_vapor_mixing_ratio_due_to_microphysics

    [4400 values with dtype=float64]

  • dq_turb
    (time, layer)
    float64
    ...

    units :

    s-1

    long_name :

    tendency_of_water_vapor_mixing_ratio_due_to_mixing

    [4400 values with dtype=float64]

  • na1
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_total_aerosol_mode1

    [4400 values with dtype=float64]

  • na2
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_total_aerosol_mode2

    [4400 values with dtype=float64]

  • na3
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_total_aerosol_mode3

    [4400 values with dtype=float64]

  • dna_micro_warm
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_aerosol_number_due_to_warm_microphysics

    [4400 values with dtype=float64]

  • dna_micro_cold
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_aerosol_number_due_to_cold_microphysics

    [4400 values with dtype=float64]

  • dna_turb
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_aerosol_number_due_to_mixing

    [4400 values with dtype=float64]

  • dni_het
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_ice_number_due_to_heterogeneous_freezing

    [4400 values with dtype=float64]

  • dni_sip
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_ice_number_due_to_secondary_ice_production

    [4400 values with dtype=float64]

  • dni_hom
    (time, layer)
    float64
    ...

    units :

    kg-1 s-1

    long_name :

    tendency_of_ice_number_due_to_homogeneous_freezing

    [4400 values with dtype=float64]

  • qlcs
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_liquid_cloud_water_in_air_stratiform

    [4400 values with dtype=float64]

  • qlrs
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_rain_water_in_air_stratiform

    [4400 values with dtype=float64]

  • qics
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_ice_cloud_in_air_stratiform

    [4400 values with dtype=float64]

  • qips
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_ice_precipitation_in_air_stratiform

    [4400 values with dtype=float64]

  • qlcc
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_liquid_cloud_water_in_air_convective

    [4400 values with dtype=float64]

  • qlrc
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_rain_water_in_air_convective

    [4400 values with dtype=float64]

  • qicc
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_ice_cloud_in_air_convective

    [4400 values with dtype=float64]

  • qipc
    (time, layer)
    float64
    ...

    units :

    kg kg-1

    long_name :

    mass_mixing_ratio_of_ice_precipitation_in_air_convective

    [4400 values with dtype=float64]

  • nlcs
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_liquid_cloud_droplets_in_air_stratiform

    [4400 values with dtype=float64]

  • nlrs
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_rain_drops_in_air_stratiform

    [4400 values with dtype=float64]

  • nics
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_ice_cloud_crystals_in_air_stratiform

    [4400 values with dtype=float64]

  • nips
    (time, layer)
    float64
    ...

    units :

    kg-1

    long_name :

    number_of_ice_precipitation_crystals_in_air_stratiform

    [4400 values with dtype=float64]

  • relcc
    (time, layer)
    float64
    ...

    units :

    m

    long_name :

    effective_radius_of_liquid_cloud_droplets_convective

    [4400 values with dtype=float64]

  • relrc
    (time, layer)
    float64
    ...

    units :

    m

    long_name :

    effective_radius_of_rain_convective

    [4400 values with dtype=float64]

  • reicc
    (time, layer)
    float64
    ...

    units :

    m

    long_name :

    effective_radius_of_ice_cloud_convective

    [4400 values with dtype=float64]

  • reipc
    (time, layer)
    float64
    ...

    units :

    m

    long_name :

    effective_radius_of_ice_precipitation_convective

    [4400 values with dtype=float64]

  • flcs
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_liquid_cloud_stratiform

    [4400 values with dtype=float64]

  • flrs
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_rain_stratiform

    [4400 values with dtype=float64]

  • fics
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_ice_cloud_stratiform

    [4400 values with dtype=float64]

  • fips
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_ice_precipitation_stratiform

    [4400 values with dtype=float64]

  • flcc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_liquid_cloud_convective

    [4400 values with dtype=float64]

  • flrc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_rain_convective

    [4400 values with dtype=float64]

  • ficc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_ice_cloud_convective

    [4400 values with dtype=float64]

  • fipc
    (time, layer)
    float64
    ...

    units :

    1

    long_name :

    area_fraction_of_ice_precipitation_convective

    [4400 values with dtype=float64]

  • vmlcs
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_liquid_cloud_water_stratiform

    [4400 values with dtype=float64]

  • vmlrs
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_rain_stratiform

    [4400 values with dtype=float64]

  • vmics
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_ice_cloud_stratiform

    [4400 values with dtype=float64]

  • vmips
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_ice_precipitation_stratiform

    [4400 values with dtype=float64]

  • vmlcc
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_liquid_cloud_water_convective

    [4400 values with dtype=float64]

  • vmlrc
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_rain_convective

    [4400 values with dtype=float64]

  • vmicc
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_cloud_ice_crystals_convective

    [4400 values with dtype=float64]

  • vmipc
    (time, layer)
    float64
    ...

    units :

    m s-1

    long_name :

    mass_weighted_fall_speed_of_ice_precipitation_convective

    [4400 values with dtype=float64]

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-12 22:30:00', '2020-03-12 23:00:00',
                   '2020-03-12 23:30:00', '2020-03-13 00:00:00',
                   '2020-03-13 00:30:00', '2020-03-13 01:00:00',
                   '2020-03-13 01:30:00', '2020-03-13 02:00:00',
                   '2020-03-13 02:30:00', '2020-03-13 03:00:00',
                   '2020-03-13 03:30:00', '2020-03-13 04:00:00',
                   '2020-03-13 04:30:00', '2020-03-13 05:00:00',
                   '2020-03-13 05:30:00', '2020-03-13 06:00:00',
                   '2020-03-13 06:30:00', '2020-03-13 07:00:00',
                   '2020-03-13 07:30:00', '2020-03-13 08:00:00',
                   '2020-03-13 08:30:00', '2020-03-13 09:00:00',
                   '2020-03-13 09:30:00', '2020-03-13 10:00:00',
                   '2020-03-13 10:30:00', '2020-03-13 11:00:00',
                   '2020-03-13 11:30:00', '2020-03-13 12:00:00',
                   '2020-03-13 12:30:00', '2020-03-13 13:00:00',
                   '2020-03-13 13:30:00', '2020-03-13 14:00:00',
                   '2020-03-13 14:30:00', '2020-03-13 15:00:00',
                   '2020-03-13 15:30:00', '2020-03-13 16:00:00',
                   '2020-03-13 16:30:00', '2020-03-13 17:00:00',
                   '2020-03-13 17:30:00', '2020-03-13 18:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • layer
    PandasIndex

    PandasIndex(Float64Index([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,
                   10.0,
                  ...
                  101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0,
                  110.0],
                 dtype='float64', name='layer', length=110))

• Attributes: (14)

  title :

  ModelE3 SCM results for COMBLE-MIP case: fixed stratiform Nd and Ni

  reference :

  https://github.com/ARM-Development/comble-mip

  authors :

  Ann Fridlind (ann.fridlind@nasa.gov), Florian Tornow (florian.tornow@nasa.gov), Andrew Ackerman (andrew.ackerman@nasa.gov)

  source :

  /data/home/fridlind/modelE3//allsteps.allmergeSCM_COMBLE-MIP_t705ml_noice.nc

  version :

  2024-05-21 23:04:13

  format_version :

  DEPHY SCM format version 1.6

  script :

  convert_ModelE3_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 :

  110