Proxy dataset - MTG Sentinel-4 UV-Visible-Near infrared (UVN) spectrometer - Nitrogen Dioxide (NO2)¶

Hint

Execute the notebook on the training platform>>

The geostationary Sentinel-4 mission will provide hourly data on tropospheric constituents over Europe for air quality applications. The target species of the Sentinel-4 mission include key air quality parameters such as nitrogen dioxide, ozone, sulphur dioxide, formaldehyde, glyoxal, and aerosols. These species will be monitored by the Sentinel-4 UVN instrument aboard the MTG Sounder satellite.

This notebook provides you an introduction to European Air Quality Forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) and ERA5 reanalysis data from the Copernicus Climate Change Service (C3S). A combination of these two dataset can be used as a proxy for data from the geostationary Sentinel-4 mission.

The event that this notebook hightlights are the fires in southern Italy and in Greece during August 2021.

Nitrogen dioxide is a useful indicator of smoke presence and transport.

Basic Facts

CAMS European Air Quality Forecasts
Spatial resolution: 0.1° x 0.1°
Spatial coverage: Europe
Temporal resolution: 1-hourly up to leadtime hour 120
Temporal coverage: three-year rolling archive
Data format: GRIB or zipped NetCDF

C3S ERA5 reanalysis
Spatial resolution: 0.25° x 0.25°
Spatial coverage: Global
Temporal resolution: 1-hourly
Temporal coverage: since 1950
Data format: GRIB or NetCDF

How to access the data

Copernicus Atmosphere Data Store (ADS)
CAMS European air quality forecasts are available for download via the Copernicus Atmosphere Data Store (ADS). You will need to create an ADS account here.

Data from the ADS can be downloaded in two ways:

manually via the ADS web interface
programmatically with a Python package called cdsapi (more information)

Copernicus Climate Change Service
C3S ERA5 reanalysis are available for download via the Copernicus Climate Data Store (CDS). You will need to create a CDS account here.

Data from the CDS can be downloaded in two ways:

manually via the CDS web interface
programmatically with a Python package called cdsapi (more information)

Load required libraries

import os
import xarray as xr
import numpy as np
import netCDF4 as nc
import pandas as pd

# Python libraries for visualization
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.cm import get_cmap
from matplotlib import animation
from matplotlib.axes import Axes
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh
from IPython.display import HTML

import warnings
warnings.simplefilter(action = "ignore", category = RuntimeWarning)
warnings.simplefilter(action = "ignore", category = UserWarning)

Load helper functions

%run ../functions.ipynb

Visualize Nitrogen Dioxide on 7 August 2021 for one time step¶

We can now visualize the created proxy resembling Level 2 Total Column Nitrogen Dioxide data collected by the Sentinel-4 mission. You can use again the pre-defined function visualize_pcolormesh.

hour = 13
visualize_pcolormesh(data_array=no2_tc_masked.isel(time=hour), 
                     longitude=no2_tc_masked.longitude, 
                     latitude=no2_tc_masked.latitude, 
                     projection=ccrs.PlateCarree(), 
                     color_scale='Spectral_r', 
                     unit= no2_conc.units, 
                     long_name= 'Simulated Sentinel-4 ' + no2_conc.species + ' \n' + str(no2_tc_masked.isel(time=hour).time.data)[0:10] \
                     + ' at ' + str(no2_tc_masked.isel(time=hour).time.data)[11:16] + ' UTC', 
                     vmin=0, 
                     vmax=10, 
                     lonmin=10,
                     lonmax=30,
                     latmin=35,
                     latmax=45,
                     set_global=False)

(<Figure size 1440x720 with 2 Axes>,
 <GeoAxesSubplot:title={'center':'Simulated Sentinel-4 Nitrogen Dioxide \n2021-08-07 at 13:00 UTC'}>)

../_images/figure6_Sentinel-4_UVN_NO2_64_1.png

Animate Total Column Nitrogen Dioxide over 24 hours¶

In the last step, you can animate the Nitrogen Dioxide over 24 hours in order to see how the trace gas developed during the 7 August 2021.

You can do animations with matplotlib’s function animation. Jupyter’s function HTML can then be used to display HTML and video content.

The animation function consists of 4 parts:

Setting the initial state:
Here, you define the general plot your animation shall use to initialise the animation. You can also define the number of frames (time steps) your animation shall have.
Functions to animate:
An animation consists of three functions: draw(), init() and animate(). draw() is the function where individual frames are passed on and the figure is returned as image. In this example, the function redraws the plot for each time step. init() returns the figure you defined for the initial state. animate() returns the draw() function and animates the function over the given number of frames (time steps).
Create a animate.FuncAnimation object:
The functions defined before are now combined to build an animate.FuncAnimation object.
Play the animation as video:
As a final step, you can integrate the animation into the notebook with the HTML class. You take the generate animation object and convert it to a HTML5 video with the to_html5_video function

# Setting the initial state:
# 1. Define figure for initial plot
fig, ax = visualize_pcolormesh(data_array=no2_tc_masked[0,:,:],
                               longitude=no2_tc_masked.longitude, 
                               latitude=no2_tc_masked.latitude,
                               projection=ccrs.PlateCarree(),
                               color_scale='Spectral_r', 
                               unit=no2_conc.units,
                               long_name= 'Simulated Sentinel-4 ' + no2_conc.species + ' \n' + str(no2_tc_masked.isel(time=0).time.data)[0:10] \
                               + ' at ' + str(no2_tc_masked.isel(time=0).time.data)[11:16] + ' UTC',
                               vmin=0,
                               vmax=10,
                               lonmin=10, 
                               lonmax=30, 
                               latmin=35, 
                               latmax=45, 
                               set_global=False)

frames = 24

def draw(i):
    img = plt.pcolormesh(no2_tc_masked.longitude, 
                         no2_tc_masked.latitude, 
                         no2_tc_masked[i,:,:], 
                         cmap='Spectral_r', 
                         transform=ccrs.PlateCarree(),
                         vmin=0,
                         vmax=10,
                         shading='auto')
    
    ax.set_title('Simulated Sentinel-4 ' + no2_conc.species + ' \n' + str(no2_tc_masked.isel(time=i).time.data)[0:10] + ' at ' + str(no2_tc_masked.isel(time=i).time.data)[11:16] + ' UTC', fontsize=20, pad=20.0)
    return img


def init():
    return fig


def animate(i):
    return draw(i)

ani = animation.FuncAnimation(fig, animate, frames, interval=800, blit=False,
                              init_func=init, repeat=True)

HTML(ani.to_html5_video())

# The following line which saves the video file is commented out
# ani.save('2021-08-07_S4_NO2.mp4')

plt.close(fig)

Play the animation video as HTML5 video¶

HTML(ani.to_html5_video())

References¶

Generated using Copernicus Atmosphere Monitoring Service Information 2021
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47
Some code in this notebook was adapted from the following source:
- origin: https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere/-/blob/master/20_data_exploration/265_CAMS_European_air_quality_forecast_NO2_load_browse.ipynb
- copyright: 2022, EUMETSAT
- license: MIT
- retrieved: 2022-06-28 by Sabrina Szeto

Return to the case study

Monitoring active fires with next-generation satellites: Mediterranean Fires Case Study
Nitrogen dioxide total column

FANGS - Fire Applications with Next-Generation Satellites

Proxy dataset - MTG Sentinel-4 UV-Visible-Near infrared (UVN) spectrometer - Nitrogen Dioxide (NO2)

Contents

Proxy dataset - MTG Sentinel-4 UV-Visible-Near infrared (UVN) spectrometer - Nitrogen Dioxide (NO2)¶

CAMS European air quality forecasts¶

Retrieve CAMS European air quality forecasts from the Atmosphere Data Store¶

Load and browse CAMS European air quality forecasts¶

Copernicus ERA-5 climate reanalysis¶

Load and browse ERA-5 climate reanalysis data¶

Interpolate ERA-5 data to a spatial resolution of CAMS European air quality forecasts¶

Cloud masking¶

Visualize Nitrogen Dioxide on 7 August 2021 for one time step¶

Animate Total Column Nitrogen Dioxide over 24 hours¶

Play the animation video as HTML5 video¶

References¶