LTPy - a Jupyter-based Learning Tool for Atmospheric Composition Data

The Copernicus programme of the European Commission makes a wealth of new environmental data available on a full, free and open license. These data include a vast range of key geophysical parameters for Earth's climate and air quality. As example, Sentinel-4 to be launched in 2023 has the potential to track for the first time over Europe diurnal variability of atmospheric compounds. Moreover, several datasets have also a sufficient temporal coverage to allow the evaluation of inter-annual variability and trends.

This leads to emerging needs to develop the capacity to access and produce usable information from one (or more) of these large datasets. Therefore, data access and training should tackle a series of challenges of technical (e.g. data format, access and dataset variety) and of scientific nature (which dataset is more appropriate for a certain application, what information can be accessed and which not).

Training shall also respond to a wide diversity of users, ranging from academic to professionals and to handling large datasets with possible limited access to network and informatics resources. The complex nature of the data, which can be data from satellite sensors and modelled data, requires a composite approach ranging from wide public information to practical examples of tools and methods how the data can be accessed, processed and visualized.

This presentation aims to present the Learning Tool for Python (LTPy), which has been developed to teach medium-level Python users how to handle, process and visualize atmospheric composition data. LTPy is based on Jupyter notebooks and has a modular approach, with learning modules on data access, data processing, data visualization and a set of example workflows to support relevant atmospheric applications. This allows users, depending on their level of experience, to choose the learning module they are mostly interested in. The modular approach further helps to broadening the user spectrum to expert- and beginner-levels at a later stage.

The tool has been developed in strong interaction with the user community and feedback from user workshops improved the final modules.

We will present the LTPy in a live-demo. The audience will have the opportunity to try out LTPy on the WEkEO cloud infrastructure as well as on their personal laptops.