{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Metop-A/B/C GOME-2 AAI product" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{hint} \n", "Execute the notebook on the training platform >>\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example introduces you to the Absorbing Aerosol Index (AAI) Level 3 data product from the GOME-2 instrument onboard the three Metop satellites Metop-A/B/C.\n", "The Absorbing Aerosol Index (AAI) indicates the presence of elevated absorbing aerosols in the Earth's atmosphere. The aerosol types that are mostly seen in the AAI are `desert dust` and `biomass burning aerosols`. The Absorbing Aerosol Index is derived from reflectances measured by GOME-2 at 340 and 380 nm. The data of the Metop-A/B/C GOME-2 `Absorbing Aerosol Index (AAI)` are provided by KNMI in the framework of the EUMETSAT Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF).\n", "\n", "Find more information on the GOME-2 Level 3 AAI data product processed by KNMI here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Basic facts\n", "**Spatial resolution**: `1° x 1°`, gridded onto a regular lat-lon grid
\n", "**Spatial coverage**: `Global`
\n", "**Temporal resolution**: `daily and monthly aggregates`
\n", "**Data availability**: `Metop-A: since 2007`, `Metop-B: since 2012`, `Metop-C: since 2019`\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} How to access the data\n", "GOME-2 AAI Level 3 data are available for download via TEMIS, a web-based service for atmospheric satellite data products maintained by KNMI. TEMIS provides daily and monthly aggregated Level 3 (gridded) data products for the three satellites Metop-A, -B, and -C. Go to the download page.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Load required libraries**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "from datetime import datetime\n", "\n", "# Python libraries for visualisation\n", "from matplotlib import pyplot as plt\n", "from matplotlib import animation\n", "\n", "import cartopy.crs as ccrs\n", "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", "\n", "from IPython.display import HTML" ] }, { "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": {}, "source": [ "## Load and browse Metop-A GOME-2 Level 3 AAI data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Metop-A/B/C GOME-3 Level 3 AAI data files can be downloaded from the TEMIS website in `NetCDF` data format. TEMIS offers the data of all three satellites Metop-A, -B and -C, which, combined, provide daily measurements for the entire globe.\n", "\n", "The following example uses daily gridded AAI data from the three satellites Metop-A, -B, and -C for 3 consecutive days between `5 to 7 February 2021`. The example shows the dispersion of aerosols during the Saharan dust event over part of south-east France, Switzerland and northern Italy.\n", "\n", "Daily gridded data is available for each satellite. Thus, the first step is to inspect one file to get a better understanding of the general data structure. Followed by loading the data files for the entire time period into one `xarray.DataArray` and to repeat this for each of the three satellites Metop-A, -B and -C." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Inspect the structure of one daily gridded AAI data file**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is in the folder `../../eodata/1_satellite/gome2/`. Since the data is distributed in the `NetCDF` format, you can use the xarray function `xr.open_dataset()` to load one single file to better understand the data structure." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                  (longitude: 360, latitude: 180)\n",
       "Coordinates:\n",
       "  * longitude                (longitude) float32 -179.5 -178.5 ... 178.5 179.5\n",
       "  * latitude                 (latitude) float32 -89.5 -88.5 -87.5 ... 88.5 89.5\n",
       "Data variables:\n",
       "    absorbing_aerosol_index  (latitude, longitude) float32 nan nan ... nan nan\n",
       "    number_of_observations   (latitude, longitude) int16 0 0 0 0 0 ... 0 0 0 0 0\n",
       "    solar_zenith_angle       (latitude, longitude) float32 nan nan ... nan nan\n",
       "Attributes: (12/32)\n",
       "    Conventions:                CF-1.6\n",
       "    title:                      ESA CCI absorbing aerosol index level 3 product\n",
       "    description:                Multi-Sensor AAI field for 05-02-2021\n",
       "    institution:                Royal Netherlands Meteorological Institute (K...\n",
       "    project:                    Climate Change Initiative - European Space Ag...\n",
       "    references:                 http://www.esa-aerosol-cci.org\n",
       "    ...                         ...\n",
       "    geospatial_lon_resolution:  1.0\n",
       "    geospatial_lat_units:       degrees_north\n",
       "    geospatial_lon_units:       degrees_east\n",
       "    comment:                    Sun glint and solar eclipse events were filte...\n",
       "    license:                    ESA CCI Data Policy: free and open access\n",
       "    summary:                    This dataset contains absorbing aerosol index...
" ], "text/plain": [ "\n", "Dimensions: (longitude: 360, latitude: 180)\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 ... 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 ... 88.5 89.5\n", "Data variables:\n", " absorbing_aerosol_index (latitude, longitude) float32 ...\n", " number_of_observations (latitude, longitude) int16 ...\n", " solar_zenith_angle (latitude, longitude) float32 ...\n", "Attributes: (12/32)\n", " Conventions: CF-1.6\n", " title: ESA CCI absorbing aerosol index level 3 product\n", " description: Multi-Sensor AAI field for 05-02-2021\n", " institution: Royal Netherlands Meteorological Institute (K...\n", " project: Climate Change Initiative - European Space Ag...\n", " references: http://www.esa-aerosol-cci.org\n", " ... ...\n", " geospatial_lon_resolution: 1.0\n", " geospatial_lat_units: degrees_north\n", " geospatial_lon_units: degrees_east\n", " comment: Sun glint and solar eclipse events were filte...\n", " license: ESA CCI Data Policy: free and open access\n", " summary: This dataset contains absorbing aerosol index..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = '../../eodata/1_satellite/gome2/ESACCI-AEROSOL-L3-AAI-GOME2A-1D-20210205-fv1.8.nc'\n", "aai_gome2a = xr.open_dataset(file)\n", "aai_gome2a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the `xarray.Dataset` above shows that one file contains the data of three variables:
\n", "* `absorbing_aerosol_index`,\n", "* `number_of_observations`, and\n", "* `solar_zenith_angle`. \n", "\n", "The variable of interest is `absorbing aerosol_index`. By adding the variable of interest into square brackets `[]`, you can select the data variable. Variables are stored as `xarray.DataArray`. You can see that the daily gridded data are on a 1 deg x 1 deg data grid, with 180 latitude values and 360 longitude values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (latitude: 180, longitude: 360)>\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",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\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", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai = aai_gome2a['absorbing_aerosol_index']\n", "aai" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Load a time-series of daily Metop-A GOME-2 Level 3 AAI data into one xarray.Dataset**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The xarray `open_mfdataset()` function allows the opening of multiple files at once. You have to specify the dimension the files shall be concatenated by. It can be an existing dimension within the data file or a new dimension, which is newly specified.\n", "\n", "Let us open the daily gridded AAI data from Metop-A for the 3 days from 5 to 7 February 2021. We specify `time` as a new dimension that the data files shall be concatenated by. After you loaded the multiple files in a `Dataset` with the function `open_mfdataset()`, you have to select `absorbing_aerosol_index` again as the variable of interest.\n", "\n", "The resulting `xarray.DataArray` has three dimensions (`time`, `latitude` and `longitude`).\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_a = xr.open_mfdataset('../../eodata/1_satellite/gome2/ESACCI-AEROSOL-L3-AAI-GOME2A-1D-2021020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_a=ds_a['absorbing_aerosol_index']\n", "aai_a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same process has to be repeated for the daily gridded AAI data from the satellites Metop-B and Metop-C respectively.\n", "Below, we load the GOME-2 Level 3 AAI data from the Metop-B satellite." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_b = xr.open_mfdataset('../../eodata/1_satellite/gome2/ESACCI-AEROSOL-L3-AAI-GOME2B-1D-2021020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_b =ds_b['absorbing_aerosol_index']\n", "aai_b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here, we load the daily gridded GOME-2 AAI Level 3 data files from the Metop-C satellite." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_c = xr.open_mfdataset('../../eodata/1_satellite/gome2/ESACCI-AEROSOL-L3-AAI-GOME2C-1D-2021020*.nc', \n", " concat_dim='time', \n", " combine='nested')\n", "\n", "aai_c=ds_c['absorbing_aerosol_index']\n", "aai_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concatenate the data from the three satellites Metop-A, -B and -C" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The overall goal is to bring the AAI data from all three satellites together. Thus, the next step is to concatenate the `DataArrays` from the three satellites Metop-A, -B and -C using a new dimension called `satellite`. \n", "You can use the `concat()` function from the xarray library to do this.\n", "\n", "The result is a four-dimensional `xarray.DataArray`, with the dimensions `satellite`, `time`, `latitude` and `longitude`.\n", "\n", "You can see that the resulting `xarray.DataArray` holds coordinate information for the two spatial dimensions `longitude` and `latitude`, but not for `time` and `satellite`.\n", "\n", "However, the coordinates for `time` will be important for plotting the data as we need to know which day the data is valid. Thus, a next step is to assign coordinates to the `time` dimension." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (satellite: 3, time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 3, 180, 360), dtype=float32, chunksize=(1, 1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Dimensions without coordinates: satellite, time\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", "Dimensions without coordinates: satellite, time\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_concat = xr.concat([aai_a,aai_b,aai_c], dim='satellite')\n", "aai_concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Assign time coordinates**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By inspecting the metadata of the single data file `aai_gome2a` we loaded at the beginning, you can see that the only metadata attribute that contains the valid time step is the `description` attribute.\n", "\n", "The first step is to retrieve the metadata attribute `description` and to split the resulting string object at the positions with a space. The day string is the fourth position of the resulting string.\n", "\n", "The `description` attribute can be accessed directly from the `aai_gome2a` `Dataset` object." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'05-02-2021'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_day = aai_gome2a.description.split()[4]\n", "start_day" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the help of the Python library `pandas`, you can build a `DateTime` time series for the three consecutive days, starting from the `start_day` variable that was defined above.\n", "\n", "You can use the `date_range` function from pandas, using the length of the time dimension of the `aai_concat` DataArray and `'d'` (for day) as freqency argument.\n", "\n", "The result is a time-series with `DateTime` information from 5 to 7 February 2021." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2021-02-05', '2021-02-06', '2021-02-07'], dtype='datetime64[ns]', freq=None)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_coords = pd.date_range(datetime.strptime(start_day,'%d-%m-%Y'), periods=len(aai_concat.time), freq='d').strftime(\"%Y-%m-%d\").astype('datetime64[ns]')\n", "time_coords" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final step is to assign the pandas time series object `time_coords` to the `aai_concat` DataArray object. You can use the `assign_coords()` function from xarray.\n", "\n", "The result is that the time coordinates have now been assigned values. The only dimension the remains unassigned is `satellite`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (satellite: 3, time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<concatenate, shape=(3, 3, 180, 360), dtype=float32, chunksize=(1, 1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "  * time       (time) datetime64[ns] 2021-02-05 2021-02-06 2021-02-07\n",
       "Dimensions without coordinates: satellite\n",
       "Attributes:\n",
       "    long_name:  Absorbing aerosol index averaged for each grid cell\n",
       "    units:      1
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", " * time (time) datetime64[ns] 2021-02-05 2021-02-06 2021-02-07\n", "Dimensions without coordinates: satellite\n", "Attributes:\n", " long_name: Absorbing aerosol index averaged for each grid cell\n", " units: 1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_concat = aai_concat.assign_coords(time=time_coords)\n", "aai_concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Combine AAI data from the three satellites onto one single grid**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the final aim is to combine the data from the three satellites Metop-A, -B and -C onto one single grid, the next step is to reduce the `satellite` dimension. You can do this by applying the reduce function `mean` to the `aai_concat` Data Array. The dimension (`dim`) to be reduced is the `satellite` dimension.\n", "\n", "This function builds the average of all data points within a grid cell. The resulting `xarray.DataArray` has three dimensions `time`, `latitude` and `longitude`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'absorbing_aerosol_index' (time: 3, latitude: 180, longitude: 360)>\n",
       "dask.array<mean_agg-aggregate, shape=(3, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * latitude   (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "  * time       (time) datetime64[ns] 2021-02-05 2021-02-06 2021-02-07
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * longitude (longitude) float32 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n", " * latitude (latitude) float32 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n", " * time (time) datetime64[ns] 2021-02-05 2021-02-06 2021-02-07" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aai_combined = aai_concat.mean(dim='satellite')\n", "aai_combined" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize GOME-2 AAI data from the three satellites Metop-A, -B and C combined on one grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to visualize the Absorbing Aerosol Index data for one time step. You can use the function `visualize_pcolormesh` for it.\n", "\n", "You can use `afmhot_r` as color map, `ccrs.PlateCarree()` as projection and by applying `dt.strftime('%Y-%m-%d').data` to the time coordinate variable, you can add the valid time step to the title of the plot.\n", "\n", "The resulting plot shows elevated AAI levels over Spain on 21 February 2021." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "visualize_pcolormesh(data_array=aai_combined[1,:,:],\n", " longitude=aai_combined.longitude, \n", " latitude=aai_combined.latitude,\n", " projection=ccrs.PlateCarree(), \n", " color_scale='afmhot_r', \n", " unit=' ',\n", " long_name=aai_a.long_name + ' ' + str(aai_combined.time[0].dt.strftime('%Y-%m-%d').data), \n", " vmin=0, \n", " vmax=5, \n", " lonmin=-50, \n", " lonmax=36, \n", " latmin=0, \n", " latmax=70.,\n", " set_global=False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Animate daily Metop-A/B/C GOME-2 L3 AAI data between 21 to 23 Feb 2021" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final step is now to animate the `aai_combined` DataArray over the 3 days to see the dispersion of Aerosols resulting from the Saharan dust event over Spain and France in February 2021.\n", "\n", "The animation function consists of four parts:\n", "- **Setting the initial state:**
\n", " Here, you define the general plot your animation shall use to initialise the animation. You can also define the number of frames (time steps) your animation shall have.\n", " \n", " \n", "- **Functions to animate:**
\n", " An animation consists of three functions: `draw()`, `init()` and `animate()`. `draw()` is the function where individual frames are passed on and the figure is returned as image. In this example, the function redraws the plot for each time step. `init()` returns the figure you defined for the initial state. `animate()` returns the `draw()` function and animates the function over the given number of frames (time steps).\n", " \n", " \n", "- **Create a `animate.FuncAnimation` object:**
\n", " The functions defined before are now combined to build an `animate.FuncAnimation` object.\n", " \n", " \n", "- **Play the animation as video:**
\n", " As a final step, you can integrate the animation into the notebook with the `HTML` class. You take the generate animation object and convert it to a HTML5 video with the `to_html5_video` function" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Setting the initial state:\n", "# 1. Define figure for initial plot\n", "fig, ax = visualize_pcolormesh(data_array=aai_combined[0,:,:],\n", " longitude=aai_combined.longitude, \n", " latitude=aai_combined.latitude,\n", " projection=ccrs.PlateCarree(), \n", " color_scale='afmhot_r', \n", " unit=' ',\n", " long_name=aai_a.long_name + '/' + str(aai_combined.time[0].dt.strftime('%Y-%m-%d').data), \n", " vmin=0, \n", " vmax=6, \n", " lonmin=-20, \n", " lonmax=36, \n", " latmin=20, \n", " latmax=70.,\n", " set_global=False)\n", "\n", "frames = 3\n", "\n", "def draw(i):\n", " img = plt.pcolormesh(aai_combined.longitude, \n", " aai_combined.latitude, \n", " aai_combined[i,:,:], \n", " cmap='afmhot_r', \n", " transform=ccrs.PlateCarree(),\n", " vmin=0,\n", " vmax=6)\n", " ax.set_title(aai_a.long_name + ' ' + str(aai_combined.time[i].dt.strftime('%Y-%m-%d').data),\n", " fontsize=20, pad=20.0)\n", " return img\n", "\n", "def init():\n", " return fig\n", "\n", "def animate(i):\n", " return draw(i)\n", "\n", "ani = animation.FuncAnimation(fig, animate, frames, interval=800, blit=False,\n", " init_func=init, repeat=True)\n", "\n", "HTML(ani.to_html5_video())\n", "plt.close(fig)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Play the animation as HTML5 video**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HTML(ani.to_html5_video())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }