Compare Data from Different Sensitivity Experiments

Here, we develop a comparison between ICON data from experiments

Load Python Libraries

[1]:

%matplotlib inline

# system libs
import os, sys, glob

# array operators and netcdf datasets
import numpy as np
import xarray as xr
xr.set_options(keep_attrs=True)

import datetime

# plotting
import pylab as plt
import matplotlib.dates as mdates
myFmt = mdates.DateFormatter('%H:%M')

import seaborn as sns
sns.set_context('talk')

# drawing onto a map
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader

[2]:

sys.path.append( '/work/bb1224/2023_MS-COURSE/tools/analysis' )
from tools import convert_timevec

Open Data with xarray

General Paths

[3]:

exercise_path = '/work/bb1224/2023_MS-COURSE'
data_path = f'{exercise_path}/data'
icon_data_path = f'{data_path}/icon-lem'

This is the content of the data directory:

[4]:

os.listdir(icon_data_path)

[4]:

['grids-extpar', 'experiments', 'bc-init']

[5]:

grid_name = 'lpz_r2'
grid_path = f'{icon_data_path}/grids-extpar/{grid_name}'

[6]:

exp_list = ['base', 'sens01', 'sens02']
# exp_list = ['base', 'sens02']
exp_main_path = f'{icon_data_path}/experiments'

Grid File

[7]:

grid = xr.open_dataset( f'{grid_path}/lpz_r2_dom01_DOM01.nc', chunks={} )

Data Files

Now, we loop over different experiment paths.

[8]:

dlist = []
for exp_name in exp_list:

    # set the file list
    file_list = f'{exp_main_path}/icon_lam_1dom_lpz-{exp_name}/2d_cloud_DOM01_ML_20210516T*Z.nc'

    # open data with xarray
    dset = xr.open_mfdataset( file_list, chunks={'time':1}, combine='by_coords' )
    dset['time'] = convert_timevec( dset.time.data )

    # create a new experiment dimension
    dset = dset.expand_dims( 'expname' )
    dset['expname'] = [exp_name, ]

    # collect all datasets in a list
    dlist += [dset, ]

# combine the listed datasets again
dset = xr.concat( dlist, dim = 'expname' )

[9]:

d_mean = dset.mean( 'ncells' )

Prepare Precip Data

[10]:

rain_mean = d_mean['rain_con_rate'] + d_mean['rain_gsp_rate']
rain_mean.attrs['long_name'] = 'rain rate'

[11]:

conversion_factor = 3600 * 24

[12]:

rain_mean = conversion_factor * rain_mean
rain_mean.attrs['units'] = 'mm day-1'

[13]:

rain_mean = rain_mean.compute()

Plotting

[14]:

fig = plt.figure( figsize = (14,6))

plt.gca().xaxis.set_major_formatter(myFmt)
rain_mean.plot( hue = 'expname', lw = 3 )
sns.despine()
../_images/nbooks_05-Compare_Data_from_different_Sensitivity_Experiments_24_0.png 
[15]:

rain_conv =  d_mean['rain_con_rate']

rain_conv = conversion_factor * rain_conv
rain_conv.attrs['units'] = 'mm day-1'

rain_conv = rain_conv.compute()

[16]:

rain_gridscale =  d_mean['rain_gsp_rate']

rain_gridscale = conversion_factor * rain_gridscale
rain_gridscale.attrs['units'] = 'mm day-1'

rain_gridscale = rain_gridscale.compute()

[17]:

fig = plt.figure( figsize = (14,6))

plt.gca().xaxis.set_major_formatter(myFmt)
rain_conv.plot( hue = 'expname', lw = 3,)

plt.gca().set_prop_cycle(None)
rain_gridscale.plot( hue = 'expname', ls = '--', lw = 1 )
sns.despine()

plt.title( 'Gridscale (dashed) vs. subgrid-scale (solid) rain', fontweight = 'bold')

[17]:

Text(0.5, 1.0, 'Gridscale (dashed) vs. subgrid-scale (solid) rain')
../_images/nbooks_05-Compare_Data_from_different_Sensitivity_Experiments_27_1.png 
[18]:

rain_mean.mean('time')

[18]:

<xarray.DataArray (expname: 3)>
array([0.48379675, 0.39261702, 0.47736754])
Coordinates:
  * expname  (expname) <U6 'base' 'sens01' 'sens02'
Attributes:
    standard_name:                rain_con_rate
    long_name:                    rain rate
    units:                        mm day-1
    param:                        76.1.0
    CDI_grid_type:                unstructured
    number_of_grid_in_reference:  1

Tasks

  • Think about the question:

    • Is convective rain changed when gridscale microphysics is switched from two-moment to one-moment bulk scheme?

    • If not, why?

  • Please explore other data with this notebook! Check how the temporal evolution looks like for

    • total cloud cover, variable name clct

    • liquid water path, variable name: tqc_dia

    • ice water path, variable name: tqi_dia