Exercise 4

Hint

Execute the notebook on the training platform >>

About

So far, we analysed Aerosol Optical Depth from different types of data (satellite, model-based and ground-based observations) for a single dust event. Let us now broaden our view and analyse the annual cycle in 2020 of Aerosol Optical Depth from AERONET and compare it with the CAMS global reanalysis data.

Tasks

1. Download and plot time-series of AERONET data for Santa Cruz, Tenerife in 2020

• Hint

  • AERONET - Example notebook

  • you can select daily aggregates of the station observations by setting the AVG key to AVG=20

• Interpret the results:

  • Have there been other times in 2020 with increased AOD values?

  • If yes, how could you find out if the increase in AOD is caused by dust? Try to find out by visualizing the AOD time-series together with another parameter from the AERONET data.

  • MSG SEVIRI Dust RGB and MODIS RGB quick looks might be helpful to get a more complete picture of other events that might have happened in 2020

2. Download CAMS global reanalysis (EAC4) and select 2020 time-series for Santa Cruz, Tenerife

• Hint

  • CAMS global forecast - Example notebook (Note: the notebook works with CAMS forecast data, but they have a similar data structure to the CAMS global reanalysis data)

  • Data access with the following specifications:

  Variable on single levels: Dust aerosol optical depth at 550 nm
  Date: Start=2020-01-01, End=2020-12-31
  Time: [00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, 21:00]
  Restricted area: N: 30., W: -20, E: 14, S: 20.
  Format: netCDF
  

  • With the xarray function sel() and keyword argument method='nearest' you can select data based on coordinate information

  • We also recommend you to transform your xarray.DataArray into a pandas.DataFrame with the function to_dataframe()

3. Visualize both time-series of CAMS reanalysis and AERONET daily aggregates in one plot

• Interpret the results: What can you say about the annual cycle in 2020 of AOD in Santa Cruz, Tenerife?