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Training users of weather and climate data through Jupyter Notebooks

Stephan Siemen, Iain Russell, Milana Vuckovic, Sylvie Lamy-Thepaut

Audience level:

Brief Summary

Python is the ideal language to process and visualise numerical weather forecasts and climate data sets. We provide many notebooks to allow users to train themselves on the various (free) data sets ECMWF and the hosted Copernicus Services (C3S, CAMS) of the European Commission offer. Notebooks are also used for training courses and training material to learn about ECMWF's open source software.


Python has become the language of choice for many users processing large data sets in the earth system sciences. This is also true for ECMWF's weather forecasts and data sets provided by the Copernicus Climate Change (C3S) and Atmospheric Monitoring Services (CAMS) that ECMWF is operating on the behalf of the European Commission. These data sets are very popular with researchers and data scientists to do their research, train their models and offer services to industry and the wider public.

ECMWF provides training for the scientific community and technical users to learn how best to interact with services, make use of its open source software and create the biggest value for their applications. Jupyter notebooks have been used in all these areas. One example is European research projects such as HiDALGO, where notebooks have been successfully used to train users on the benefits of weather and climate data and provided examples on how to access data across various data centres. A large set of notebooks has been developed to provide examples on how to use the various Python open source software packages, which ECMWF provides for the community.

A new Python package called CliMetLab, aimed at data scientists using Machine Learning on weather and climate data has from the beginning been developed to be used in JupyterLab environments. It integrates closely with the PyData eco system to ease the migration.

This poster will present how users interested in weather and climate data can, with the help of Python, gain access to large earth systems data sets and achieve everything from data download, decoding, processing to visualisation. The poster will link to the various resources to try out notebooks developed at ECMWF.