Atmospheric physics

Publications on climate modelling

*by student I (co)-advised
  1. *Heuer, H., M. Schwabe, P. Gentine, M. A. Giorgetta, V. Eyring. Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON. Submitted to JAMES (2023).

Presentations on climate modelling at international workshops and conferences

Talks unless otherwise noted
  1. Schwabe, M., Bonnet, P., Grundner, A., Schlund, M. and Eyring, V.: "ML developments for ICON and Evaluation with ESMValTool". ICON Seamless Workshop, 10.-11.08.2023, Offenbach, Deutschland (2023)
  2. Schwabe, M. and Eyring, V. "Machine learning for improved understanding and projections of climate change." TRR 165/181 Conference on ”Scale interactions, data-driven modeling, and uncertainty in weather and climate”, 27.-30. Oct. 2023, Ingolstadt. (2023) (invited)
  3. Schwabe, M., Pastori, L., Dogra, L., Klamt, J., Sarauer, E., Eyring, V.: Quantum Machine Learning for Climate Science. Workshop on Applications of Quantum Computing, 10.-11. Jul. 2023, Garching (2023) (Poster, invited)
  4. Schwabe, M. and Shamekh, S. – Teaser Talk: Hybrid Modelling For Atmosphere , USMILE General Assembly, Valencia, Spain (2022) (invited)
  5. Schwabe, M., Behrens, G., Beucler, T., Iglesias-Suarez, F., Gentine, P., Giorgetta, M., Grundner, A., Pritchard, M., Eyring, V.: "Interpretable AI – two examples", ELLIS Workshop, Valencia, Spain (2022)
  6. Schwabe, M., Grundner, A., Gentine, P., Giorgetta, M. A., Rapp, M., Eyring, V.: "Machine Learning based gravity wave parameterizations for ICON", 2022 SPARC Gravity Wave Symposium, online (Poster) (2022)