Polar Multi-Sensor Aerosol Optical Properties (PMAp) Product - Aerosol Optical Depth - Level 2¶

About¶

The following module shows you the structure of PMAp Aerosol Optical Depth Level 2 data and what information of the data files can be used in order to load, browse and visualize Aerosol Optical Depth at 550 nm.

The polar multi-sensor aerosol optical properties product (PMAp) provides information on aerosol optical depth (AOD) at 550 nm and aerosol type (fine mode, coarse mode (dust) and volcanic ash) over ocean and land surfaces. The product is generated based on the Meteosat Second Generation instrument GOME-2 with support of information from the instruments AVHRR and IASI.

Find more information about the product in this factsheet.

Basic Facts¶

Spatial resolution: Metop-A: 5km x 40 km, Metop-B: 10km x 40 km
Spatial coverage: Global
Data availability: since February 2014

How to access the data¶

PMAp data can be ordered via the EUMETSAT Data Centre, which you can access via the EUMETSAT Earth Observation Portal. If you do not have an account for the EUMETSAT EO Portal, you can create an account here.

Module outline¶

• 1 - Load and browse PMAp Aerosol Optical Depth Level 2 data files
• 2 - Load multiple PMAp Level 2 data files as xarray.DataArray object
• 3 - Visualize PMAp Aerosol Optical Depth Level 2 data

Load required libraries¶

Load helper functions¶

1. Load and browe PMAp Aerosol Optical Depth Level 2 data¶

PMAp Level 2 data is disseminated in the NetCDF format. You can use the netCDF4 Python library to access and manipulate the data.

Structure of PMAp Aerosol Optical Depth Level 2 data files¶

The PMAp data files are structured in 15 groups:

• conventions
• Data
• disposition_mode
• keywords
• metadata_conventions
• orbit_end
• orbit_start
• organization
• product_level
• product_name
• sensing_end_time_utc
• sensing_start_time_utc
• spacecraft
• Status
• summary

Most groups provide metadata information. The group of interest that contains the data itself is Data/MeasurementData, which has the sub-groups GeoData and ObservationData:

• MeasurementData
  • GeoData
    • aerosol_center_latitude
    • aerosol_center_longitude
    • aerosol_corner_latitude
    • aerosol_corner_longitude
    • aerosol_sensor_readout_start_time
    • cloud_center_latitude
    • cloud_center_longitude
    • cloud_corner_latitude
    • cloud_corner_longitude
    • day
    • hour
    • minute
    • month
    • year
    • ...
  • ObservationData
    • Aerosol
      • aerosol_class
      • aerosol_optical_depths
      • aerosol_retrievals_flag
      • Auxiliary
    • Cloud
      • Auxiliary
      • cloud_optical_depth
      • cloud_retrieval_flags
      • cloud_top_temperature
    • QualityInformation
      • AVHRR
      • ECMWF
      • ...

Thus, the aerosol data information can be found under the group ObservationData/Aerosol and the corresponding geo information under the group GeoData/.

Load a PMAp Level 2 data file with the netCDF4 library¶

With the Dataset() function of the netCDF4 library, you can load a single file with the NetCDF format. PMAp data can be found in the folder ../../../eodata/dust/part1/1_satellite/pmap/06/.

The resulting object is a netCDF4.Dataset object, which acts like a Python dictionary. Thus, with the keys() function you can list the different groups the file contains.

You can now select the Data/MeasurementData/GeoData group and get a list of available variables under this group.

You can do the same for the group Data/MeasurementData/ObservationData/Aerosol/.

Load multiple PMAp data files as xarray.DataArray object¶

For easier handling and plotting of the PMAp data, you can combine geolocation information and aerosol data values in a xarray.DataArray structure, which can be created with the xarray.DataArray constructor.

You can make use of the function load_l2_data_xr, which loads a PMAp Level 2 dataset and returns a xarray.DataArray with all the ground pixels of all files in a directory.

The function takes following arguments:

• directory: directory where the HDF file(s) are stored
• internal_filepath: internal path of the data variable that is of interest, e.g. Data/MeasurementData/ObservationData/Aerosol/
• parameter: paramter that is of interest, e.g. aerosol_optical_depth
• lat_path: path of the latitude variable
• lon_path: path of the longitude variable
• no_of_dims: number of dimensions of input array
• paramname: name of the parameter, preferable taken from the data file
• unit: unit of the parameter, preferably taken from the data file
• longname: longname of the parameter, preferably taken from the data file

Let us start with the retrieval of the Aerosol Optical Depth information from the data file loaded above. From there, you are able to retrieve the parameter's attributes for Units, Standard_name and Long_name.

The next step is to construct a xarray.DataArray by using the load_l2_data_xr function. We define the objects latpath, lonpath and internal_filepath.

Let us construct the DataArray object and call it aod_xr. The resulting object is a one-dimensional xarray.DataArray with more than 1.2 Mio ground pixel values and latitude and longitude as coordinate information.

Generate a geographical subset for the Mediterranean region¶

Let us now create a geographic subset for the Mediterranean region. You can use the function generate_geographical_subset to create a subset based on latitude and longitude information. You do not want to reassign the longitude values, e.g. to bring the values from a [0,360] grid to a [-180, 180] grid. For this reason, you set this keyword argument to False.

You see that the subsetting data array only has a bit more than 62.000 pixel information.

Visualize PMAp Aerosol Optical Depth Level 2 data¶

The final step is to visualize the PMAp Aerosol Optical Depth Level 2 data. Since the final data object is a one-dimensional xarray.DataArray, you can take advantage of the function visualize_scatter. The function creates a matplotlib scatter plot.

The function takes the following arguments as input:

• xr_dataarray: your xarray data object
• conversion factor: conversion factor in case the data values are very small, otherwise it is 1
• projection: cartopy projection
• vmin: minimum number on visualisation legend
• vmax: maximum number on visualisation legend
• point size: size of the marker
• color scale: color palette
• unit: unit of the variable
• title: long name of the variable
• set_global: if yes, the resulting plot will have a global coverage

This project is licensed under GNU General Public License v3.0 only and is developed under a Copernicus contract.

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