{
"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 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": [
"
<xarray.Dataset>\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] 2021-08-...\n", " time_utc (time, scanline) object '2021-08-05T11:2...\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 ...
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(['2021-08-05T00:00:00.000000000'], dtype='datetime64[ns]')
array([0., 1., 2., 3.])
[1877400 values with dtype=float32]
[1877400 values with dtype=float32]
array([['2021-08-05T11:21:47.152000000', '2021-08-05T11:21:47.992000000',\n", " '2021-08-05T11:21:48.832000000', ..., '2021-08-05T12:20:09.031000000',\n", " '2021-08-05T12:20:09.871000000', '2021-08-05T12:20:10.711000000']],\n", " dtype='datetime64[ns]')
array([['2021-08-05T11:21:47.152000Z', '2021-08-05T11:21:47.992000Z',\n", " '2021-08-05T11:21:48.832000Z', ..., '2021-08-05T12:20:09.031000Z',\n", " '2021-08-05T12:20:09.871000Z', '2021-08-05T12:20:10.711000Z']],\n", " dtype=object)
[1877400 values with dtype=float32]
[1877400 values with dtype=float32]
[1877400 values with dtype=float32]
[1877400 values with dtype=float32]
[1877400 values with dtype=float32]
<xarray.DataArray 'aerosol_index_340_380' (scanline: 243, ground_pixel: 359)>\n", "array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n", "Coordinates:\n", " * scanline (scanline) float64 2.36e+03 2.361e+03 ... 2.601e+03 2.602e+03\n", " * ground_pixel (ground_pixel) float64 77.0 78.0 79.0 ... 433.0 434.0 435.0\n", " time datetime64[ns] 2021-08-05\n", " latitude (scanline, ground_pixel) float32 32.12 32.14 ... 46.68 46.68\n", " longitude (scanline, ground_pixel) float32 11.19 11.26 ... 29.7 29.8\n", "Attributes:\n", " units: 1\n", " proposed_standard_name: ultraviolet_aerosol_index\n", " comment: Aerosol index from 380 and 340 nm\n", " long_name: Aerosol index from 380 and 340 nm\n", " radiation_wavelength: [340. 380.]\n", " ancillary_variables: aerosol_index_340_380_precision
array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)
array([2360., 2361., 2362., ..., 2600., 2601., 2602.])
array([ 77., 78., 79., ..., 433., 434., 435.])
array('2021-08-05T00:00:00.000000000', dtype='datetime64[ns]')
array([[32.12033 , 32.14433 , 32.168037, ..., 34.96878 , 34.966583,\n", " 34.964294],\n", " [32.16859 , 32.192604, 32.216316, ..., 35.017277, 35.015064,\n", " 35.012764],\n", " [32.216835, 32.240856, 32.264584, ..., 35.06577 , 35.063545,\n", " 35.061234],\n", " ...,\n", " [43.59572 , 43.622807, 43.64955 , ..., 46.589333, 46.584236,\n", " 46.57898 ],\n", " [43.64298 , 43.670074, 43.696842, ..., 46.63765 , 46.632534,\n", " 46.62727 ],\n", " [43.690258, 43.71738 , 43.744156, ..., 46.686016, 46.68088 ,\n", " 46.675602]], dtype=float32)
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