Python Plotting 2. Global plot of Nitrogen oxide dataset sourced from GOME -2 satellite datasets

The Global Ozone Monitoring Experiment–2 (GOME-2) was primarily launched to get information of the total atmospheric content of ozone and the vertical ozone profile in the atmosphere.It also provides accurate information on the total column amount of nitrogen dioxide, sulphur dioxide, water vapour, oxygen/oxygen dimmer, bromine oxide and other trace gases, as well as aerosols.

Today we will try to plot the global tropospheric NO2 column using simple libraries of python namely xarray, cartopy and matplotlib.

Data is sourced from : https://atmos.eoc.dlr.de/products/

I will be using level 3 NO2 global data from the available products for the year 2016.

The data-set are available in netcdf(.nc) format.

Lets begin !!!:

#% /usr/bin/env python
import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import cartopy.feature as ftr
from datetime import datetime
from matplotlib import pyplot as plt
date=datetime(2016,1,1)
#Make a loop to open all the months one after the other
mean_value=0

##### If you wish to calculate for seasonal #### specify dates as follows

#Winter
#all_date=[datetime(2016,1,1),datetime(2016,2,1),datetime(2016,12,1)]
#spring
#all_date=[datetime(2016,3,1),datetime(2016,4,1),datetime(2016,5,1)]
#summer
#all_date=[datetime(2016,6,1),datetime(2016,7,1),datetime(2016,8,1)]
#autumn
#all_date=[datetime(2016,9,1),datetime(2016,10,1),datetime(2016,11,1)]

all_date=[datetime(2016,1,1),datetime(2016,2,1),datetime(2016,3,1),datetime(2016,4,1),datetime(2016,5,1),datetime(2016,6,1),datetime(2016,7,1),datetime(2016,8,1),datetime(2016,9,1),datetime(2016,10,1),datetime(2016,11,1),datetime(2016,12,1)]
for date in all_date:
    path="D:/CAS/UCAS_Study/Conference/ACAM Malayasia conference/Miniproject/NO2 GOME/"+date.strftime('%Y')+" NO2/"
    file_name="GOME_NO2_Global_"+date.strftime('%Y%m')+"_METOPA_DLR_v1.nc"
    data=xr.open_dataset(path+file_name)
    prod=xr.open_dataset(path+file_name, group="PRODUCT")
    data["NO2trop"]=prod.NO2trop
    plt_data=data.sel({"latitude": slice(-90,90),"longitude": 
slice(-180,180)})      ####Global plot ########
     mean_value=(mean_value+plt_data.NO2trop)

#Figure size
fig=plt.figure(figsize=[20,16])
#fig.set_dpi(400)
minv=0
maxv=1.5e16
levels=np.log10(np.array((np.arange(100)+1)*(maxv-minv)/99+minv))

spl=plt.axes(projection=ccrs.PlateCarree())

ax=plt.contourf(plt_data.longitude,plt_data.latitude,np.log10(mean_value/len(all_date)),levels=levels,\transform=ccrs.PlateCarree(),zorder=0,cmap='jet')

plt.title(date.strftime('2016_GlobeNO2'))

#show study area, specific point

#plt.scatter(94.73,29.77,transform=ccrs.PlateCarree(),c='r',s=10)

spl.set_xticks([-180,-120,-60,0,60,120,180],crs=ccrs.PlateCarree())

spl.set_yticks([-90,-60,-30,0,30,60,90],crs=ccrs.PlateCarree())

spl.coastlines()

spl.add_feature(ftr.BORDERS)

#for the side bar coordinate

#spl.set_xticks([50,60,70,80,90,100,110],crs=ccrs.PlateCarree())

#spl.set_yticks([20,30,40],crs=ccrs.PlateCarree())

#spl.gridlines()

#fig.add_axes([0.1])

fig.colorbar(ax,label="log10 "+prod.NO2trop.units,orientation="horizontal")

plt.savefig('NO2'+date.strftime('2016_GlobeNO2')+'.png', bbox_inches='tight')

Global plot

Special Thanks to my friend and colleague who helped me with scripting : Mr. Hemraj Bhattarai

(Chinese Academy of Sciences)