{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"# Sentinel-5P TROPOMI UVAI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{hint} \n",
"Execute the notebook on the training platform >>\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"tags": []
},
"source": [
"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.\n",
"\n",
"\n",
"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.\n",
"\n",
"Sentinel-5P carries the `TROPOMI` instrument, which is a spectrometer in the UV-VIS-NIR-SWIR spectral range. `TROPOMI` provides measurements on:\n",
"* `Ozone`\n",
"* `NO``2`\n",
"* `SO``2`\n",
"* `Formaldehyde`\n",
"* `Aerosol`\n",
"* `Carbonmonoxide`\n",
"* `Methane`\n",
"* `Clouds`\n",
"\n",
"Read more information about Sentinel-5P here."
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"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 Sentinel-5P Pre-Operations Data Hub. You can login with the following credentials:\n",
"* **Username**: `s5pguest`\n",
"* **Password**: `s5pguest`\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
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array([0.000e+00, 1.000e+00, 2.000e+00, ..., 4.169e+03, 4.170e+03, 4.171e+03])
array([ 0., 1., 2., ..., 447., 448., 449.])
array(['2021-02-06T00:00:00.000000000'], dtype='datetime64[ns]')
array([0., 1., 2., 3.])
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array([['2021-02-06T12:28:48.299000000', '2021-02-06T12:28:49.139000000',\n", " '2021-02-06T12:28:49.979000000', ..., '2021-02-06T13:27:10.180000000',\n", " '2021-02-06T13:27:11.020000000', '2021-02-06T13:27:11.860000000']],\n", " dtype='datetime64[ns]')
array([['2021-02-06T12:28:48.299000Z', '2021-02-06T12:28:49.139000Z',\n", " '2021-02-06T12:28:49.979000Z', ..., '2021-02-06T13:27:10.180000Z',\n", " '2021-02-06T13:27:11.020000Z', '2021-02-06T13:27:11.860000Z']],\n", " dtype=object)
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<xarray.DataArray 'aerosol_index_354_388' (time: 1, scanline: 4172, ground_pixel: 450)>\n", "[1877400 values with dtype=float32]\n", "Coordinates:\n", " * scanline (scanline) float64 0.0 1.0 2.0 ... 4.17e+03 4.171e+03\n", " * ground_pixel (ground_pixel) float64 0.0 1.0 2.0 3.0 ... 447.0 448.0 449.0\n", " * time (time) datetime64[ns] 2021-02-06\n", " latitude (time, scanline, ground_pixel) float32 ...\n", " longitude (time, scanline, ground_pixel) float32 ...\n", "Attributes:\n", " units: 1\n", " proposed_standard_name: ultraviolet_aerosol_index\n", " comment: Aerosol index from 388 and 354 nm\n", " long_name: Aerosol index from 388 and 354 nm\n", " radiation_wavelength: [354. 388.]\n", " ancillary_variables: aerosol_index_354_388_precision
[1877400 values with dtype=float32]
array([0.000e+00, 1.000e+00, 2.000e+00, ..., 4.169e+03, 4.170e+03, 4.171e+03])
array([ 0., 1., 2., ..., 447., 448., 449.])
array(['2021-02-06T00:00:00.000000000'], dtype='datetime64[ns]')
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<xarray.DataArray 'aerosol_index_354_388' (scanline: 4172, ground_pixel: 450)>\n", "[1877400 values with dtype=float32]\n", "Coordinates:\n", " * scanline (scanline) float64 0.0 1.0 2.0 ... 4.17e+03 4.171e+03\n", " * ground_pixel (ground_pixel) float64 0.0 1.0 2.0 3.0 ... 447.0 448.0 449.0\n", " time datetime64[ns] 2021-02-06\n", " latitude (scanline, ground_pixel) float32 ...\n", " longitude (scanline, ground_pixel) float32 ...\n", "Attributes:\n", " units: 1\n", " proposed_standard_name: ultraviolet_aerosol_index\n", " comment: Aerosol index from 388 and 354 nm\n", " long_name: Aerosol index from 388 and 354 nm\n", " radiation_wavelength: [354. 388.]\n", " ancillary_variables: aerosol_index_354_388_precision
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array([0.000e+00, 1.000e+00, 2.000e+00, ..., 4.169e+03, 4.170e+03, 4.171e+03])
array([ 0., 1., 2., ..., 447., 448., 449.])
array('2021-02-06T00:00:00.000000000', dtype='datetime64[ns]')
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