Sentinel-5P TROPOMI UVAI

Sentinel-5P TROPOMI UVAI#

Hint

Execute the notebook on the training platform >>

The following example introduces you to the Aerosol Index (AI) product from the Sentinel-5P TROPOMI instrument. The Aerosol Index (AI) is a qualitative index indicating the presence of elevated layers of aerosols with significant absorption. The main aerosol types that cause signals detected in the AI are desert dust, biomass burning and volcanic ash plumes. An advantage of the AI is that it can be derived for clear as well as (partly) cloudy ground pixels.

The Copernicus Sentinel-5 Precursor mission is the first Copernicus mission dedicated to atmospheric monitoring. The main objective of the Sentinel-5P mission is to perform atmospheric measurements with high spatio-temporal resolution, to be used for air quality, ozone & UV radiation, and climate monitoring and forecasting.

Sentinel-5P carries the TROPOMI instrument, which is a spectrometer in the UV-VIS-NIR-SWIR spectral range. TROPOMI provides measurements on:

Ozone
NO₂
SO₂
Formaldehyde
Aerosol
Carbonmonoxide
Methane
Clouds

Read more information about Sentinel-5P here.

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 data are disseminated in the netCDF format and can be downloaded as a zipped archive via the Copernicus Data Space:
Step 1: Register and create an account.
Step 2: Go to the Sentinel-5P data collection.
Step 3: Go to SEARCH and search for Sentinel-5P Level-2 AER-AI data and select a date range (e.g 8 April 2024) and region of interest (e.g. bounding box covering the Mediterranean sea).
Step 4: Select a file and download it.

Load required libraries

import xarray as xr

import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.cm import get_cmap
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

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

Load helper functions

%run ../../functions.ipynb

Visualize Sentinel-5P TROPOMI aerosol index from 388 and 354 nm#

The next step is to visualize the dataset. You can use the function visualize_pcolormesh, which makes use of matploblib’s function pcolormesh and the Cartopy library.

With ?visualize_pcolormesh you can open the function’s docstring to see what keyword arguments are needed to prepare your plot.

?visualize_pcolormesh

You can make use of the variables we have defined above:

units
long_name
latitude
longitude

Additionally, you can specify the color scale and minimum and maxium data values.

visualize_pcolormesh(data_array=ai_0804,
                     longitude=ai_0804.longitude,
                     latitude=ai_0804.latitude,
                     projection=ccrs.PlateCarree(),
                     color_scale='hot_r',
                     unit=units,
                     long_name=longname + ' ' + str(ai_0804.time.data),
                     vmin=0, 
                     vmax=3)

(<Figure size 2000x1000 with 2 Axes>,
 <GeoAxes: title={'center': 'Aerosol index from 354 and 388 nm 2024-04-08T00:00:00.000000000'}>)

../_images/a2a07dbb8292f42d5163a00782b8d2be227947f94d5d8ead4295961057b470bf.png

Sentinel-5P TROPOMI UVAI

Contents

Sentinel-5P TROPOMI UVAI#

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

Visualize Sentinel-5P TROPOMI aerosol index from 388 and 354 nm#

Create a geographical subset for Europe#