ESMAC: Generate timeseries of size distribution

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Setup dependencies:

import os
import numpy as np
import xarray as xr
import pandas as pd
# these sys settings are just for the jupyterhub demo
import sys
sys.path.append('/home/'+os.environ['USER']+'/.local/lib/python3.9/site-packages')
import esmac_diags.plotting.plot_esmac_diags as plot
import matplotlib.dates as mdates

Matplotlib created a temporary config/cache directory at /tmp/matplotlib-u1qg630p because the default path (/home/jovyan/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.

Configure settings:

# set site name.
site = 'HISCALE'

# path of prepared files

data_path = '~/ARM-Notebooks/Open-Science-Workshop-2022/tutorials/ESMAC_diags/ESMAC_Diags_testcase/prep_data/'

prep_model_path = data_path +site+'/model/'
prep_obs_path = data_path +site+'/surface/'
prep_obs_path_2 = data_path +site+'/satellite/'
# set output path for plots
figpath= '~/ARM-Notebooks/Open-Science-Workshop-2022/tutorials/ESMAC_diags/ESMAC_Diags_testcase/figures/'+site+'/surface/'

Read data:

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# read in data


# trim for the same time period
IOP = 'IOP1'
time1 = np.datetime64('2016-04-25')
time2 = np.datetime64('2016-05-22')
time = pd.date_range(start='2016-04-25', end='2016-05-22', freq="H")
# IOP = 'IOP2'
# time1 = np.datetime64('2016-08-28')
# time2 = np.datetime64('2016-09-23')
# time = pd.date_range(start='2016-08-28', end='2016-09-23', freq="H")

filename = prep_obs_path + 'sfc_SMPS_'+site+'_'+IOP+'.nc'
obsdata = xr.open_dataset(filename)
time_smps = obsdata['time'].load()
smpsall = obsdata['dN_dlogDp'].load()
size_smps = obsdata['size'].load()
obsdata.close()

filename = prep_model_path + 'E3SMv1_'+site+'_sfc.nc'
modeldata = xr.open_dataset(filename)
time_m1 = modeldata['time'].load()
ncn_e3sm1 = modeldata['NCNall'].load()
modeldata.close()

filename = prep_model_path + 'E3SMv2_'+site+'_sfc.nc'
modeldata = xr.open_dataset(filename)
time_m2 = modeldata['time'].load()
ncn_e3sm2 = modeldata['NCNall'].load()
modeldata.close()

ncn_e3sm1 = ncn_e3sm1[:, np.logical_and(time_m1>=time1, time_m1<=time2)]
ncn_e3sm2 = ncn_e3sm2[:, np.logical_and(time_m2>=time1, time_m2<=time2)]
smpsall = smpsall[np.logical_and(time_smps>=time1, time_smps<=time2),:]

Data treatment:

# convert to dN/dlog10Dp
# SMPS is already dlogDp
dlogDp_e3sm = np.log10(np.arange(2,3002)/np.arange(1,3001))
ncn_e3sm1 = ncn_e3sm1.T/dlogDp_e3sm
ncn_e3sm2 = ncn_e3sm2.T/dlogDp_e3sm
    

Generate plot:

if not os.path.exists(figpath):
    os.makedirs(figpath)

fig,ax = plot.timeseries_size([time, time, time], [size_smps, np.arange(1,3001), np.arange(1,3001)], 
                        [smpsall.data.T, ncn_e3sm1.data.T, ncn_e3sm2.data.T], legend = ['SMPS','E3SMv1','E3SMv2'], 
                        xlabel='Time', ylabel='Diameter (nm)', ylimit=(3,1000),
                         title= 'Aerosol Size Distribution (dN/dlogDp, cm$^{-3}$)')
ax[0].set_xlim(ax[1].get_xlim())

ax[0].xaxis.set_minor_locator(mdates.DayLocator(interval=1))
ax[1].xaxis.set_minor_locator(mdates.DayLocator(interval=1))
ax[2].xaxis.set_minor_locator(mdates.DayLocator(interval=1))
ax[0].xaxis.set_major_locator(mdates.DayLocator(interval=5))
ax[1].xaxis.set_major_locator(mdates.DayLocator(interval=5))
ax[2].xaxis.set_major_locator(mdates.DayLocator(interval=5))
#fig.savefig(figpath+'timeseries_AerosolSize_'+site+'_'+IOP+'.png',dpi=fig.dpi,bbox_inches='tight', pad_inches=1)

# show figures in interactive commandline screen
import matplotlib.pyplot as plt
plt.show()