Mar 6 – 7, 2023
Meteorological Institute, University of Cologne
Europe/Berlin timezone
Registration and abstract submission are closed. We are looking forward to meet you!

Machine learning (ML) is rapidly gaining momentum as a new toolbox for analysing atmospheric data. While there are now several workshops, fora and conferences to discuss ML applications in the weather and climate domain, discussions on ML applications for air quality remain fragmented. The ERC project IntelliAQ has explored several modern ML concepts for air quality research and we would like to engage in a discussion with the international community about the potential and limitations of ML in this field. 


The workshop aims to bring together researchers from the air quality and machine learning communities for discussion of recent research progress and future priorities. We encourage oral and poster contributions from researchers in either of these areas, and particularly welcome contributions at the intersection of these fields.


We are looking forward to meet you in Cologne or online. 

Meteorological Institute, University of Cologne
Lecture hall 4th floor and lobby
Pohligstraße 3 50969 Köln Germany
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There is no registration fee for this meeting, but we expect you to cover your own travel expenses (flight and accomodation). There are plenty of hotels in Cologne. Prices and availability may vary depending on trade fairs etc. We suggest to look at the hotel "Ibis Koeln Centrum" ( Rooms are around 90-100 €/night. Mention that you are a guest of the University of Cologne if you are booking.


This workshop will be followed by a workshop of the Tropospheric Ozone Assessment Report at the same location (separate registration at Note, however, that this ML workshop is independent from the TOAR-II workshop and has no specific focus on ozone-related research.


Scientific coordinators:

Martin Schultz, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany

Paul Griffiths, Yusuf Hamied Department of Chemistry, National Centre for Atmospheric Science, Cambridge University, UK