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LTPy - Learning Tool for Python on Atmospheric Composition Data


LTPy - Learning tool for Python on Atmospheric Composition Data is a Python-based training course on Atmospheric Composition Data. The training course covers modules on data access, data handling and processing, data visualisation as well as case studies of satellite- and model-based data on Atmospheric Composition.

The course is based on Jupyter notebooks, which allow for a high-level of interactive learning, as code, text description and visualisation is combined in one place. If you have not worked with Jupyter Notebooks before, you can look at the module 01 - Introduction to Python and Project Jupyter to get a short introduction to Jupyter notebooks and their benefits.

Data on Atmospheric Composition

This course features the following data:

  • AC SAF GOME-2 Level-2 data onboard of Metop-A and Metop-B satellites
  • AC SAF GOME-2 Level-3 reprocessed and regridded data

  • GOME-2 Level-2 Polar Multi-Sensor Aerosol Optical Properties (PMAp) data onboard of Metop-A and Metop-B satellites

  • IASI Level-2 data onboard of Metop-A and Metop-B satellites

  • Copernicus Sentinel-5P data
  • Copernicus Sentinel-3 OLCI data
  • Copernicus Sentinel-3 SLSTR NRT FRP data
  • Copernicus Atmosphere Monitoring Service (CAMS) data

Course material

The course follows a modular approach and offers the following modules:

NOTE: Throughout the course, general functions to load, re-shape, process and visualize the datasets are defined. These functions are re-used when applicable. The LTPy functions notebook gives you an overview of all the functions defined and used for the course.

The notebook 12 - WEkEO Harmonized Data Access API makes use of functions defined in the LTPy HDA API functions notebook.

Learning outcomes

The course is designed for medium-level users, who have basic Python knowledge and understanding of Atmospheric composition data.

After the course, you should have:

  • an idea about the different datasets on Atmospheric Composition data,
  • knowledge about the most useful Python packages to handle, process and visualise large volumes of Earth Observation data
  • an idea about different data application areas

Access to the LTPy JupyterHub

The course material is made available on a JupyterHub instance, a pre-defined environment that give learners direct access to the data and Python packages required for following the course.

The JupyterHub can be accessed as follows:


Reproduce LTPy on Atmospheric Compostion data locally

In case you wish to reproduce the course modules on your local setup, the following Python version and Python packages will be required:

Python packages can be installed with conda install <python_package_name> or pip install <python_pacakage_name>.

The eodata folder with all the data required for the training course can be downloaded here.


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This project is licensed under the MIT License View on GitLab | EUMETSAT Training