{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Copernicus Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{hint} \n",
"Execute the notebook on the training platform >>\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Copernicus [Sentinel-5 Ultraviolet Visible Near-Infrared Shortwave (UVNS) spectrometer](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) enables the measurement of trace gases which will improve air quality forecasts produced by the Copernicus Atmosphere Monitoring service.\n",
"\n",
"This notebook provides you an introduction to data from [Sentinel-5P](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p), the precursor instrument and proxy for data from [Sentinel-5](https://www.eumetsat.int/sentinel-5). \n",
"\n",
"The event that this notebook hightlights are the fires in southern Italy and in Greece during August 2021."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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: \n",
"- `smoke from forest fires`, \n",
"- `volcanic ash`, or \n",
"- `desert dust`. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{admonition} Basic Facts\n",
"**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) \n",
"**Spatial coverage**: `Global` \n",
"**Revisit time**: `less than one day` \n",
"**Data availability**: `since April 2018`\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{admonition} How to access the data\n",
"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. \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Load required libraries**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from datetime import datetime\n",
"from netCDF4 import Dataset\n",
"\n",
"# Python libraries for visualization\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors\n",
"from matplotlib.axes import Axes\n",
"import cartopy.crs as ccrs\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"from cartopy.mpl.geoaxes import GeoAxes\n",
"GeoAxes._pcolormesh_patched = Axes.pcolormesh\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Load helper functions**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%run ../functions.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"tags": []
},
"source": [
"## Load and browse Sentinel-5P TROPOMI Aerosol Index Level 2 data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Sentinel-5P TROPOMI UVAI Level 2 file is organised in two groups: `PRODUCT` and `METADATA`. The `PRODUCT` group stores the main data fields of the product, including `latitude`, `longitude` and the variable itself. The `METADATA` group provides additional metadata items.\n",
"\n",
"Sentinel-5P TROPOMI variables have the following dimensions:\n",
"* `scanline`: the number of measurements in the granule / along-track dimension index\n",
"* `ground_pixel`: the number of spectra in a measurement / across-track dimension index\n",
"* `time`: time reference for the data\n",
"* `corner`: pixel corner index\n",
"* `layer`: this dimension indicates the vertical grid of profile variables\n",
"\n",
"Sentinel-5P TROPOMI data is disseminated in `netCDF`. You can load a netCDF file with the `open_dataset()` function of the xarray library. In order to load the variable as part of a Sentinel-5P data files, you have to specify the following keyword arguments: \n",
"* `group='PRODUCT'`: to load the `PRODUCT` group. \n",
"\n",
"Let us load a Sentinel-5P TROPOMI data file as `xarray.Dataset` from 5 August 2021 and inspect the data structure:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"Collapsed": "false",
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"
This coordinate variable defines the indices for the pixel corners; index starts at 0 (counter-clockwise, starting from south-western corner of the pixel in ascending part of the orbit)
A continuous quality descriptor, varying between 0 (no data) and 1 (full quality data). Recommend to ignore data with qa_value < 0.5
[1877400 values with dtype=float32]
aerosol_index_354_388
(time, scanline, ground_pixel)
float32
...
units :
1
proposed_standard_name :
ultraviolet_aerosol_index
comment :
Aerosol index from 388 and 354 nm
long_name :
Aerosol index from 388 and 354 nm
radiation_wavelength :
[354. 388.]
ancillary_variables :
aerosol_index_354_388_precision
[1877400 values with dtype=float32]
aerosol_index_340_380
(time, scanline, ground_pixel)
float32
...
units :
1
proposed_standard_name :
ultraviolet_aerosol_index
comment :
Aerosol index from 380 and 340 nm
long_name :
Aerosol index from 380 and 340 nm
radiation_wavelength :
[340. 380.]
ancillary_variables :
aerosol_index_340_380_precision
[1877400 values with dtype=float32]
aerosol_index_354_388_precision
(time, scanline, ground_pixel)
float32
...
units :
1
proposed_standard_name :
ultraviolet_aerosol_index standard_error
comment :
Precision of aerosol index from 388 and 354 nm
long_name :
Precision of aerosol index from 388 and 354 nm
radiation_wavelength :
[354. 388.]
[1877400 values with dtype=float32]
aerosol_index_340_380_precision
(time, scanline, ground_pixel)
float32
...
units :
1
proposed_standard_name :
ultraviolet_aerosol_index standard_error
comment :
Precision of aerosol index from 380 and 340 nm
long_name :
Precision of aerosol index from 380 and 340 nm
radiation_wavelength :
[340. 380.]
[1877400 values with dtype=float32]
"
],
"text/plain": [
"\n",
"Dimensions: (scanline: 4172, ground_pixel: 450, time: 1, corner: 4)\n",
"Coordinates:\n",
" * scanline (scanline) float64 0.0 1.0 ... 4.171e+03\n",
" * ground_pixel (ground_pixel) float64 0.0 1.0 ... 449.0\n",
" * time (time) datetime64[ns] 2021-08-05\n",
" * corner (corner) float64 0.0 1.0 2.0 3.0\n",
" latitude (time, scanline, ground_pixel) float32 ...\n",
" longitude (time, scanline, ground_pixel) float32 ...\n",
"Data variables:\n",
" delta_time (time, scanline) datetime64[ns] ...\n",
" time_utc (time, scanline) object ...\n",
" qa_value (time, scanline, ground_pixel) float32 ...\n",
" aerosol_index_354_388 (time, scanline, ground_pixel) float32 ...\n",
" aerosol_index_340_380 (time, scanline, ground_pixel) float32 ...\n",
" aerosol_index_354_388_precision (time, scanline, ground_pixel) float32 ...\n",
" aerosol_index_340_380_precision (time, scanline, ground_pixel) float32 ..."
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s5p = xr.open_dataset('../data/sentinel-5p/uvai/2021/08/05/S5P_OFFL_L2__AER_AI_20210805T110012_20210805T124141_19750_02_020200_20210807T004652.nc', group='PRODUCT')\n",
"s5p"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Retrieve the Aerosol index from 340 and 380 nm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Sentinel-5P TROPOMI aerosol index data provide the Aerosol Index for two different wavelength pairs: \n",
"- `340_380 nm`\n",
"- `354_388 nm` \n",
"\n",
"For both parameters the interpretation of the index value is the same. \n",
"\n",
"Let us use the aerosol index for the wavelength pair 340_380 nm. The variable name is `aerosol_index_340_380`. You can select the variable from the data set, together with the geo-coordinates as follows:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"uvai = s5p.aerosol_index_340_380[0,:,:]\n",
"lat = uvai.latitude\n",
"lon = uvai.longitude\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read and apply the Quality Flag to the UVAI data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another important parameter to read is the general quality flag called `qa_value`. The quality flag removes the sun glint pixels, where \"falsely\" high positive UVAI values over sea can be detected. In order to exclude sun glint, it is recommended to use only values where the `qa_value` is higher than 0.8. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"qa=s5p.qa_value[0,:,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final step before plotting is to mask the aerosol index data based on the qa mask. You filter data with the `xarray` function `where`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"uvai_masked=uvai.where(qa > 0.8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"## Create a geographical subset around Italy and Greece"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"You can zoom into a region by specifying a `bounding box` of interest. Let us set the extent to Italy and Greece with the following bounding box information:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"lonmin=10\n",
"lonmax=30\n",
"latmin=35\n",
"latmax=45"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use the function [](functions:generate_geographical_subset) to subset an `xarray.DataArray` based on a given bounding box."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"