Copernicus Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2¶

Hint

Execute the notebook on the training platform >>

The Copernicus Sentinel-5 Ultraviolet Visible Near-Infrared Shortwave (UVNS) spectrometer enables the measurement of trace gases which will improve air quality forecasts produced by the Copernicus Atmosphere Monitoring service.

This notebook provides you an introduction to data from Sentinel-5P, the precursor instrument and proxy for data from Sentinel-5.

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

For monitoring smoke, the TROPOMI UV Aerosol Index (UVAI) data can be used. Positive values of UVAI (typically > about 1.0) indicate the presence of absorbing-type aerosols:

smoke from forest fires,
volcanic ash, or
desert dust.

Basic Facts

Spatial resolution: Up to 5.5* km x 3.5 km (5.5 km in the satellite flight direction and 3.5 km in the perpendicular direction at nadir)
Spatial coverage: Global
Revisit time: less than one day
Data availability: since April 2018

How to access the data

Sentinel-5P Pre-Ops data are disseminated in the netCDF format and can be downloaded via the Copernicus Open Access Hub. You need to register for an account before downloading data.

Load required libraries

import os
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
from netCDF4 import Dataset

# Python libraries for visualization
import matplotlib.cm as cm
import matplotlib.colors
from matplotlib.axes import Axes
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh

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

Load helper functions

%run ../functions.ipynb

Visualise the Sentinel-5P TROPOMI UV Aerosol Index values¶

For visualization, you can use the function visualize_pcolormesh to visualize the data. The following keyword arguments have to be defined:

data_array
longitude
latitude
projection
color palette
unit
long_name
vmin,
vmax
extent (lonmin, lonmax, latmin, latmax)
set_global

visualize_pcolormesh(data_array=s5p_uvai_subset,
                     longitude=s5p_uvai_subset.longitude, 
                     latitude=s5p_uvai_subset.latitude, 
                     projection=ccrs.PlateCarree(), 
                     color_scale='afmhot_r', 
                     unit=uvai.units, 
                     long_name=uvai.long_name + ' ' + str(uvai.time.data)[0:10], 
                     vmin=0, 
                     vmax=6, 
                     lonmin=lonmin, 
                     lonmax=lonmax, 
                     latmin=latmin, 
                     latmax=latmax, 
                     set_global=False)

(<Figure size 1440x720 with 2 Axes>,
 <GeoAxesSubplot:title={'center':'Aerosol index from 380 and 340 nm 2021-08-05'}>)

../_images/figure4_Sentinel-5P_TROPOMI_UVAI_33_1.png

References¶

Copernicus Sentinel data 2021
Some code in this notebook was adapted from the following source:
- origin: https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere/-/blob/master/20_data_exploration/242_Sentinel-5P_TROPOMI_UVAI_L2_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
Ultraviolet Aerosol Index

FANGS - Fire Applications with Next-Generation Satellites

Copernicus Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2

Contents

Copernicus Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2¶

Load and browse Sentinel-5P TROPOMI Aerosol Index Level 2 data¶

Retrieve the Aerosol index from 340 and 380 nm¶

Read and apply the Quality Flag to the UVAI data¶

Create a geographical subset around Italy and Greece¶

Visualise the Sentinel-5P TROPOMI UV Aerosol Index values¶

References¶