C3S Aerosol Properties Gridded Data - Level 3¶
About¶
This notebooks provides an introduction to the C3S Aerosol properties gridded data from 1995 to present derived from satellite observations and shows you how the variable Dust Aerosol Optical Depth can be used to monitor and predict dust events.
According to C3S, "This data set provides observational records of aerosol properties obtained from observations collected by various satellite instruments.
The main variables provided by this dataset are: aerosol optical depth, fine mode aerosol optical depth, dust aerosol optical depth, single scattering albedo, aerosol layer height and aerosol extinction coefficient. These variables are derived from observations from several sensors using a set of different processing techniques.
Selected observational records in this dataset are extended in time on a semi-annual basis. At the moment of extending, these records are up-to-date until five months behind present time." (Source)
The notebook examines the Saharan Dust event which occured over Europe at the beginning of February 2021.
Basic Facts¶
Spatial resolution:
2.5° x 2.5° for aerosol extinction coefficient, 1° x 1° for all other variables
Spatial coverage:Global
Temporal resolution:5-daily composite for the aerosol extinction coefficient, Daily and monthly for all other variables
Temporal coverage:since June 1995 to present with five month delay
Data format:NetCDF (compressed either as a zip or tar file)
How to access the data¶
C3S aerosol properties gridded data are available for download via the Copernicus Climate Data Store (CDS). You will need to create a CDS account here.
Data from the CDS can be downloaded in two ways:
manuallyvia the CDS web interfaceprogrammaticallywith a Python package called cdsapi (more information)
Module outline¶
- Optional: Retrieve C3S aerosol properties gridded data programmatically
- 1 - Load and browse dust aerosol optical depth (duaod) at 550nm of the CAMS global near-real-time forecast
- 2 - Retrieve the time coordinate information and assign time coordinates for the time dimension
- 3 - Retrieve the data variable dust AOD at 550nm as xarray.DataArray
- 4 - Visualize dust AOD at 550nm
- 5 - Create a geographical subset for Europe
- 6 - Animate dust AOD at 550nm changes over time
Load required libraries¶
import xarray as xr
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.cm import get_cmap
from matplotlib import animation
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
from IPython.display import HTML
import warnings
warnings.simplefilter(action = "ignore", category = RuntimeWarning)
Load helper functions¶
%run ../functions.ipynb
Optional: Retrieve C3S aerosol properties gridded data programmatically¶
The CDS Application Program Interface (CDS API) is a Python library which allows you to access data from the CDS programmatically. In order to use the CDS API, follow the steps below:
- Self-register at the CDS registration page (if you do not have an account yet)
- Login to the CDS portal and go to the api-how-to page
- Copy the CDS API key displayed in the black terminal window and replace the
######of theKEYvariable below with your individual CDS API key
Note: You find your CDS API key displayed in the black terminal box under the section Install the CDS API key. If you do not see a URL or key appear in the black terminal box, please refresh your browser tab.
URL='https://cds.climate.copernicus.eu/api/v2'
KEY='#########################'
The next step is then to request the data with a so called API request. Via the CDS web interface, you can select the data and at the end of the web interface, you can open the CDS request via Show API request. Copy the request displayed there in the cell below. Once you execute the cell, the download of the data starts automatically. Note that the code cell below is commented out. You need to delete the ''' blockquotes to uncomment it for the cell to run.
'''
import cdsapi
c = cdsapi.Client(url=URL, key=KEY)
c.retrieve(
'satellite-aerosol-properties',
{
'format': 'zip',
'time_aggregation': 'daily_average',
'variable': 'dust_aerosol_optical_depth',
'sensor_on_satellite': 'iasi_on_metopb',
'algorithm': 'ulb',
'year': '2021',
'month': '02',
'day': [
'04', '05', '06',
'07', '08',
],
'version': 'v9',
'orbit': 'ascending',
},
'./download.zip')
'''
"\n\nimport cdsapi\n\nc = cdsapi.Client(url=URL, key=KEY)\n\nc.retrieve(\n 'satellite-aerosol-properties',\n {\n 'format': 'zip',\n 'time_aggregation': 'daily_average',\n 'variable': 'dust_aerosol_optical_depth',\n 'sensor_on_satellite': 'iasi_on_metopb',\n 'algorithm': 'ulb',\n 'year': '2021',\n 'month': '02',\n 'day': [\n '04', '05', '06',\n '07', '08',\n ],\n 'version': 'v9',\n 'orbit': 'ascending',\n },\n './download.zip')\n\n"
C3S aerosol properties gridded data can be retrieved in a zipped NetCDF or a tar-compressed NetCDF. Above, we requested the data in a zipped NetCDF and for this reason, we have to unzip the file before we can open it. You can unzip zip archives in Python with the Python package zipfile and the function extractall(). You will see a five new files appearing in the same folder as this notebook. This is just for demonstration purposes.
import zipfile
with zipfile.ZipFile('../../../eodata/dust/part1/1_satellite/iasi_c3s/2021/02/download.zip', 'r') as zip_ref:
zip_ref.extractall('./')
1. Load and browse C3S aerosol properties gridded data¶
C3S aerosol properties gridded data is available as netCDF.
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.
Let us open the daily gridded aerosol properties data from C3S Metop-B IASI for the 5 days from 4 to 8 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.
The resulting xarray.DataArray has three dimensions (time, latitude and longitude).
file = xr.open_mfdataset('../../../eodata/dust/part1/1_satellite/iasi_c3s/2021/02/*C3S-L3_AEROSOL-AER_PRODUCTS-IASI-METOPB-ULB-DAILY-v9AN.nc',
concat_dim='time',
combine='nested')
file
<xarray.Dataset>
Dimensions: (latitude: 180, longitude: 360, time: 5)
Coordinates:
* latitude (latitude) float64 -89.5 -88.5 ... 88.5 89.5
* longitude (longitude) float64 -179.5 -178.5 ... 179.5
Dimensions without coordinates: time
Data variables:
number (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD10000_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD10000_uncertainty_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD11000_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD550_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Detection_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes: (12/43)
Title: Dust Aerosol Optical Depth retrieved from IAS...
Institution: Universite Libre de Bruxelles (ULB)
Source: IASI L1C, IASI L2 NRT (EUMETSAT)
History: 2022-05-14 11:00:31 - Product generated from ...
References: ULBNN ATBD issue 1.1
Tracking_id: 8572ebff-3b19-4f81-9264-7e7c5df8a7a7
... ...
dateTime: 0051-02-03 00:00:00
productID: 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB...
platform: MetOpA
sensor: IASI
spatial_resolution: 12 km diameter pixel at nadir
inputFileList: , g:\dust\v00009\Data\L2\2021\20210204-C3S-L2...- latitude: 180
- longitude: 360
- time: 5
- latitude(latitude)float64-89.5 -88.5 -87.5 ... 88.5 89.5
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) - longitude(longitude)float64-179.5 -178.5 ... 178.5 179.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
- number(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Number of observations averaged
- standard_name :
- number_averaged
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD10000_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 10000 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD10000_uncertainty_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth uncertainty at 10000 nm
- standard_name :
- uncertainty_on_atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD11000_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 11000 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD550_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 550 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - Detection_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Percentage number of dust detections
- standard_name :
- Percentage_number_of_dust_detections
- units :
- 1
- valid_range :
- [ 0. 100.]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray
- Title :
- Dust Aerosol Optical Depth retrieved from IASI measurements in the Thermal Infrared (also converted to 550nm) - ULBNN v00009
- Institution :
- Universite Libre de Bruxelles (ULB)
- Source :
- IASI L1C, IASI L2 NRT (EUMETSAT)
- History :
- 2022-05-14 11:00:31 - Product generated from ULBNN v00009
- References :
- ULBNN ATBD issue 1.1
- Tracking_id :
- 8572ebff-3b19-4f81-9264-7e7c5df8a7a7
- Conventions :
- CF-1.6
- Level 3 :
- lon = 360, lat = 180, time = 28
- Product_Version :
- v00009
- Summary :
- This data set contains the dust AOD (at 10 micron and 550 nm) from the ULBNN retrieval algorithm
- keywords :
- satellite, observation, atmosphere, sand, dust, aerosol optical depth
- id :
- 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
- naming authority :
- ulb.ac.be
- keywords_vocabulary :
- GCMD Science Keywords
- cdm_data_type :
- swath
- comment :
- This dataset was produced as part of the C3S_312a_Lot5 project
- date_created :
- 2022-05-14 11:00:31
- creator_name :
- ULB, Universite libre de Bruxelles - Lieven Clarisse
- creator_url :
- ulb.ac.be/cpm
- creator_email :
- lieven.clarisse@ulb.ac.be
- project :
- Climate Change Initiative - European Space Agency
- geospatial_lat_min :
- -90.0
- geospatial_lat_max :
- 90.0
- geospatial_long_min :
- -180.0
- geospatial_long_max :
- 180.0
- geospatial_vertical_min :
- 0.0
- geospatial_vertical_max :
- 10000.0
- time_coverage_start :
- 00510203T000000Z
- time_coverage_end :
- 00510203T234108Z
- time_coverage_duration :
- P1Y
- time_coverage_resolution :
- P1D
- standard_name_vocabulary :
- NetCDF Climate and Forecast (CF) Metadata Convention version 18
- geospatial_lat_units :
- degrees_north
- geospatial_lon_units :
- degrees_east
- geospatial_lat_resolution :
- 1.0
- geospatial_lon_resolution :
- 1.0
- startDate :
- 0051-02-03
- dateTime :
- 0051-02-03 00:00:00
- productID :
- 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
- platform :
- MetOpA
- sensor :
- IASI
- spatial_resolution :
- 12 km diameter pixel at nadir
- inputFileList :
- , g:\dust\v00009\Data\L2\2021\20210204-C3S-L2P_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
The data above has three dimensions (latitude, longitude, time) and several data variables. The data variable of interest is:
D_AOD550_mean: Dust Aerosol Optical Depth at 550nm
Let us inspect the coordinates of the file more in detail.
The latitude values have a 1 degree resolution and have a global N-S coverage.
file.latitude
<xarray.DataArray 'latitude' (latitude: 180)>
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
-69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
-59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
-49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
-29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
-19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
-9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5,
0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5,
20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5,
30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5,
40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5,
50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5,
60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5,
70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5,
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
Coordinates:
* latitude (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
Attributes:
long_name: latitude
standard_name: latitude
units: degrees_north
valid_range: [-90. 90.]- latitude: 180
- -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 ... 84.5 85.5 86.5 87.5 88.5 89.5
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) - latitude(latitude)float64-89.5 -88.5 -87.5 ... 88.5 89.5
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
The longitude values have a 1 degree resolution as well, and are disseminated in a [0, 360] grid.
file.longitude
<xarray.DataArray 'longitude' (longitude: 360)>
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
Coordinates:
* longitude (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5
Attributes:
long_name: longitude
standard_name: longitude
units: degrees_east
valid_range: [-180. 180.]- longitude: 360
- -179.5 -178.5 -177.5 -176.5 -175.5 ... 175.5 176.5 177.5 178.5 179.5
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
- longitude(longitude)float64-179.5 -178.5 ... 178.5 179.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
2. Retrieve time coordinate information and assign time coordinates for the time dimension¶
By inspecting the metadata of the single data file file we loaded at the beginning, you can see that the metadata attribute id contains the date in the name.
The first step is to retrieve the metadata attribute id and to split the resulting string object at the positions with a dash -. The day string is the first position of the resulting string.
start_day = file.id.split('-')[0]
start_day
'20210204'
With the help of the Python library pandas, you can build a DateTime time series for the five consecutive days, starting from the start_day variable that was defined above.
You can use the date_range function from pandas, using the length of the time dimension of the file DataArray and 'd' (for day) as freqency argument.
The result is a time-series with DateTime information from 4 to 8 February 2021.
time_coords = pd.date_range(datetime.strptime(start_day,'%Y%m%d'), periods=len(file.time), freq='d').strftime("%Y-%m-%d").astype('datetime64[ns]')
time_coords
DatetimeIndex(['2021-02-04', '2021-02-05', '2021-02-06', '2021-02-07',
'2021-02-08'],
dtype='datetime64[ns]', freq=None)
The final step is to assign the pandas time series object time_coords to the file DataArray object. You can use the assign_coords() function from xarray.
The result is that the time coordinates have now been assigned values. The only dimension the remains unassigned is satellite.
file = file.assign_coords(time=time_coords)
file
<xarray.Dataset>
Dimensions: (latitude: 180, longitude: 360, time: 5)
Coordinates:
* latitude (latitude) float64 -89.5 -88.5 ... 88.5 89.5
* longitude (longitude) float64 -179.5 -178.5 ... 179.5
* time (time) datetime64[ns] 2021-02-04 ... 2021-02-08
Data variables:
number (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD10000_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD10000_uncertainty_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD11000_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
D_AOD550_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Detection_mean (time, latitude, longitude) float64 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes: (12/43)
Title: Dust Aerosol Optical Depth retrieved from IAS...
Institution: Universite Libre de Bruxelles (ULB)
Source: IASI L1C, IASI L2 NRT (EUMETSAT)
History: 2022-05-14 11:00:31 - Product generated from ...
References: ULBNN ATBD issue 1.1
Tracking_id: 8572ebff-3b19-4f81-9264-7e7c5df8a7a7
... ...
dateTime: 0051-02-03 00:00:00
productID: 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB...
platform: MetOpA
sensor: IASI
spatial_resolution: 12 km diameter pixel at nadir
inputFileList: , g:\dust\v00009\Data\L2\2021\20210204-C3S-L2...- latitude: 180
- longitude: 360
- time: 5
- latitude(latitude)float64-89.5 -88.5 -87.5 ... 88.5 89.5
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) - longitude(longitude)float64-179.5 -178.5 ... 178.5 179.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
- time(time)datetime64[ns]2021-02-04 ... 2021-02-08
array(['2021-02-04T00:00:00.000000000', '2021-02-05T00:00:00.000000000', '2021-02-06T00:00:00.000000000', '2021-02-07T00:00:00.000000000', '2021-02-08T00:00:00.000000000'], dtype='datetime64[ns]')
- number(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Number of observations averaged
- standard_name :
- number_averaged
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD10000_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 10000 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD10000_uncertainty_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth uncertainty at 10000 nm
- standard_name :
- uncertainty_on_atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD11000_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 11000 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - D_AOD550_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Dust Aerosol Optical Depth at 550 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - Detection_mean(time, latitude, longitude)float64dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
- long_name :
- Percentage number of dust detections
- standard_name :
- Percentage_number_of_dust_detections
- units :
- 1
- valid_range :
- [ 0. 100.]
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray
- Title :
- Dust Aerosol Optical Depth retrieved from IASI measurements in the Thermal Infrared (also converted to 550nm) - ULBNN v00009
- Institution :
- Universite Libre de Bruxelles (ULB)
- Source :
- IASI L1C, IASI L2 NRT (EUMETSAT)
- History :
- 2022-05-14 11:00:31 - Product generated from ULBNN v00009
- References :
- ULBNN ATBD issue 1.1
- Tracking_id :
- 8572ebff-3b19-4f81-9264-7e7c5df8a7a7
- Conventions :
- CF-1.6
- Level 3 :
- lon = 360, lat = 180, time = 28
- Product_Version :
- v00009
- Summary :
- This data set contains the dust AOD (at 10 micron and 550 nm) from the ULBNN retrieval algorithm
- keywords :
- satellite, observation, atmosphere, sand, dust, aerosol optical depth
- id :
- 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
- naming authority :
- ulb.ac.be
- keywords_vocabulary :
- GCMD Science Keywords
- cdm_data_type :
- swath
- comment :
- This dataset was produced as part of the C3S_312a_Lot5 project
- date_created :
- 2022-05-14 11:00:31
- creator_name :
- ULB, Universite libre de Bruxelles - Lieven Clarisse
- creator_url :
- ulb.ac.be/cpm
- creator_email :
- lieven.clarisse@ulb.ac.be
- project :
- Climate Change Initiative - European Space Agency
- geospatial_lat_min :
- -90.0
- geospatial_lat_max :
- 90.0
- geospatial_long_min :
- -180.0
- geospatial_long_max :
- 180.0
- geospatial_vertical_min :
- 0.0
- geospatial_vertical_max :
- 10000.0
- time_coverage_start :
- 00510203T000000Z
- time_coverage_end :
- 00510203T234108Z
- time_coverage_duration :
- P1Y
- time_coverage_resolution :
- P1D
- standard_name_vocabulary :
- NetCDF Climate and Forecast (CF) Metadata Convention version 18
- geospatial_lat_units :
- degrees_north
- geospatial_lon_units :
- degrees_east
- geospatial_lat_resolution :
- 1.0
- geospatial_lon_resolution :
- 1.0
- startDate :
- 0051-02-03
- dateTime :
- 0051-02-03 00:00:00
- productID :
- 20210204-C3S-L3_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
- platform :
- MetOpA
- sensor :
- IASI
- spatial_resolution :
- 12 km diameter pixel at nadir
- inputFileList :
- , g:\dust\v00009\Data\L2\2021\20210204-C3S-L2P_AEROSOL-D_AOD-IASI-MetopB-ULB-v00009-AN-fv1.nc
3. Retrieve the variable Dust Aerosol Optical Depth at 550nm as data array¶
Let us extract from the dataset above the data variable Dust Aerosol Optical Depth (AOD) at 550nm as xarray.DataArray with the name du_aod. You can load a data array from a xarray dataset by specifying the name of the variable (D_AOD550_mean) in square brackets.
du_aod = file['D_AOD550_mean']
du_aod
<xarray.DataArray 'D_AOD550_mean' (time: 5, latitude: 180, longitude: 360)>
dask.array<concatenate, shape=(5, 180, 360), dtype=float64, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
* latitude (latitude) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
* longitude (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5
* time (time) datetime64[ns] 2021-02-04 2021-02-05 ... 2021-02-08
Attributes:
long_name: Dust Aerosol Optical Depth at 550 nm
standard_name: atmosphere_optical_thickness_due_to_aerosol
units: 1
valid_range: [ 0. inf]- time: 5
- latitude: 180
- longitude: 360
- dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Array Chunk Bytes 2.47 MiB 506.25 kiB Shape (5, 180, 360) (1, 180, 360) Count 20 Tasks 5 Chunks Type float64 numpy.ndarray - latitude(latitude)float64-89.5 -88.5 -87.5 ... 88.5 89.5
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) - longitude(longitude)float64-179.5 -178.5 ... 178.5 179.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5])
- time(time)datetime64[ns]2021-02-04 ... 2021-02-08
array(['2021-02-04T00:00:00.000000000', '2021-02-05T00:00:00.000000000', '2021-02-06T00:00:00.000000000', '2021-02-07T00:00:00.000000000', '2021-02-08T00:00:00.000000000'], dtype='datetime64[ns]')
- long_name :
- Dust Aerosol Optical Depth at 550 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Next, we can fill all NaN values with a large negative number to prevent the piling up of pixels in our animation later on. We will use a colormap later that has a white color for values at or below 0.
du_aod = du_aod.fillna(-999)
Above, you see that the variable du_aod has an attribute called long_name. Let us define variables for this attribute so it can be used later for visualizing the data.
long_name = du_aod.long_name
Let us do the same for the coordinates longitude and latitude.
latitude = du_aod.latitude
longitude = du_aod.longitude
4. Visualize Dust Aerosol Optical Depth at 550nm¶
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
Signature: visualize_pcolormesh( data_array, longitude, latitude, projection, color_scale, unit, long_name, vmin, vmax, set_global=True, lonmin=-180, lonmax=180, latmin=-90, latmax=90, ) Docstring: Visualizes a xarray.DataArray with matplotlib's pcolormesh function. Parameters: data_array(xarray.DataArray): xarray.DataArray holding the data values longitude(xarray.DataArray): xarray.DataArray holding the longitude values latitude(xarray.DataArray): xarray.DataArray holding the latitude values projection(str): a projection provided by the cartopy library, e.g. ccrs.PlateCarree() color_scale(str): string taken from matplotlib's color ramp reference unit(str): the unit of the parameter, taken from the NetCDF file if possible long_name(str): long name of the parameter, taken from the NetCDF file if possible vmin(int): minimum number on visualisation legend vmax(int): maximum number on visualisation legend set_global(boolean): optional kwarg, default is True lonmin,lonmax,latmin,latmax(float): optional kwarg, set geographic extent is set_global kwarg is set to False File: /tmp/ipykernel_1451/2163691773.py Type: function
You can make use of the variables we defined above:
unitslong_namelatitudelongitude
Additionally, you can specify the color scale and minimum (vmin) and maxium (vmax) data values.
time_index = 2
visualize_pcolormesh(data_array=du_aod[time_index,:,:],
longitude=longitude,
latitude=latitude,
projection=ccrs.PlateCarree(),
color_scale='afmhot_r',
unit=' ',
long_name=long_name + ' ' + str(du_aod[time_index,:,:].time.data)[0:10],
vmin=0,
vmax=1)
(<Figure size 1440x720 with 2 Axes>,
<GeoAxesSubplot:title={'center':'Dust Aerosol Optical Depth at 550 nm 2021-02-06'}>)
5. Create a geographical subset for Europe¶
The map above shows Dust Aerosol Optical Depth at 550nm globally. Let us create a geographical subset for Europe, in order to better analyse the Saharan dust event which impacts parts of central and southern Europe.
For geographical subsetting, you can use the function generate_geographical_subset. You can use ?generate_geographical_subset to open the docstring in order to see the function's keyword arguments.
?generate_geographical_subset
Signature: generate_geographical_subset( xarray, latmin, latmax, lonmin, lonmax, reassign=False, ) Docstring: Generates a geographical subset of a xarray.DataArray and if kwarg reassign=True, shifts the longitude grid from a 0-360 to a -180 to 180 deg grid. Parameters: xarray(xarray.DataArray): a xarray DataArray with latitude and longitude coordinates latmin, latmax, lonmin, lonmax(int): lat/lon boundaries of the geographical subset reassign(boolean): default is False Returns: Geographical subset of a xarray.DataArray. File: /tmp/ipykernel_1451/3979307327.py Type: function
Define the bounding box information for Europe
latmin = 28.
latmax = 71.
lonmin = -22.
lonmax = 43
Now, let us apply the function generate_geographical_subset to subset the du_aod xarray.DataArray. Let us call the new xarray.DataArray du_aod_subset.
du_aod_subset = generate_geographical_subset(xarray=du_aod,
latmin=latmin,
latmax=latmax,
lonmin=lonmin,
lonmax=lonmax)
du_aod_subset
<xarray.DataArray 'D_AOD550_mean' (time: 5, latitude: 43, longitude: 65)>
dask.array<where, shape=(5, 43, 65), dtype=float64, chunksize=(1, 43, 65), chunktype=numpy.ndarray>
Coordinates:
* latitude (latitude) float64 28.5 29.5 30.5 31.5 ... 67.5 68.5 69.5 70.5
* longitude (longitude) float64 -21.5 -20.5 -19.5 -18.5 ... 40.5 41.5 42.5
* time (time) datetime64[ns] 2021-02-04 2021-02-05 ... 2021-02-08
Attributes:
long_name: Dust Aerosol Optical Depth at 550 nm
standard_name: atmosphere_optical_thickness_due_to_aerosol
units: 1
valid_range: [ 0. inf]- time: 5
- latitude: 43
- longitude: 65
- dask.array<chunksize=(1, 43, 65), meta=np.ndarray>
Array Chunk Bytes 109.18 kiB 21.84 kiB Shape (5, 43, 65) (1, 43, 65) Count 51 Tasks 5 Chunks Type float64 numpy.ndarray - latitude(latitude)float6428.5 29.5 30.5 ... 68.5 69.5 70.5
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
array([28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5]) - longitude(longitude)float64-21.5 -20.5 -19.5 ... 41.5 42.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
array([-21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5]) - time(time)datetime64[ns]2021-02-04 ... 2021-02-08
array(['2021-02-04T00:00:00.000000000', '2021-02-05T00:00:00.000000000', '2021-02-06T00:00:00.000000000', '2021-02-07T00:00:00.000000000', '2021-02-08T00:00:00.000000000'], dtype='datetime64[ns]')
- long_name :
- Dust Aerosol Optical Depth at 550 nm
- standard_name :
- atmosphere_optical_thickness_due_to_aerosol
- units :
- 1
- valid_range :
- [ 0. inf]
Let us visualize the subsetted xarray.DataArray again. This time, you set the set_global kwarg to False and you specify the longitude and latitude bounds specified above.
Additionally, in order to have the time information as part of the title, we add the string of the datetime information to the long_name variable: long_name + ' ' + str(du_aod_subset[##,:,:].time.data)[0:10].
time_index = 2
visualize_pcolormesh(data_array=du_aod_subset[time_index,:,:],
longitude=du_aod_subset.longitude,
latitude=du_aod_subset.latitude,
projection=ccrs.PlateCarree(),
color_scale='afmhot_r',
unit=' ',
long_name=long_name + ' ' + str(du_aod_subset[time_index,:,:].time.data)[0:10],
vmin=0,
vmax=1,
lonmin=lonmin,
lonmax=lonmax,
latmin=latmin,
latmax=latmax,
set_global=False)
(<Figure size 1440x720 with 2 Axes>,
<GeoAxesSubplot:title={'center':'Dust Aerosol Optical Depth at 550 nm 2021-02-06'}>)
6. Animate changes of Dust Aerosol Optical Depth at 550nm over time¶
In the last step, you can animate the Dust Aerosol Optical Depth at 550nm in order to see how the trace gas develops over a period of 5 days, from 4th to 8th February 2021.
You can do animations with matplotlib's function animation. Jupyter's function HTML can then be used to display HTML and video content.
The animation function consists of 4 parts:
- Setting the initial state:
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.
- Functions to animate:
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).
- Create a
animate.FuncAnimationobject:
The functions defined before are now combined to build an animate.FuncAnimation object.
- Play the animation as video:
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
# Setting the initial state:
# 1. Define figure for initial plot
fig, ax = visualize_pcolormesh(data_array=du_aod_subset[0,:,:],
longitude=du_aod_subset.longitude,
latitude=du_aod_subset.latitude,
projection=ccrs.PlateCarree(),
color_scale='afmhot_r',
unit=' ',
long_name=long_name + ' '+ str(du_aod_subset.time[0].data)[0:10],
vmin=0,
vmax=1,
lonmin=lonmin,
lonmax=lonmax,
latmin=latmin,
latmax=latmax,
set_global=False)
frames = 5
def draw(i):
img = plt.pcolormesh(du_aod_subset.longitude,
du_aod_subset.latitude,
du_aod_subset[i,:,:],
cmap='afmhot_r',
transform=ccrs.PlateCarree(),
vmin=0,
vmax=1,
shading='auto')
ax.set_title(long_name + ' '+ str(du_aod_subset.time[i].data)[0:10], fontsize=20, pad=20.0)
return img
def init():
return fig
def animate(i):
return draw(i)
ani = animation.FuncAnimation(fig, animate, frames, interval=800, blit=False,
init_func=init, repeat=True)
HTML(ani.to_html5_video())
plt.close(fig)
Play the animation video as HTML5 video¶
HTML(ani.to_html5_video())
References¶
- Copernicus Service information 2023
- Generated using Copernicus Atmosphere Monitoring Service Information 2023
This project is licensed under GNU General Public License v3.0 only and is developed under a Copernicus contract.