Towards a Data-Driven Understanding of Cloud Structure Formation (Papers Track)

Ann-Christin Wörl (Johannes Gutenberg University); Michael Wand (University of Mainz); Peter Spichtinger (Johannes Gutenberg University)

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Interpretable ML Climate Science & Modeling


The physics of cloud formation and evolution is still not fully understood and constitutes one of the highest uncertainties in climate modeling. We are working on an approach that aims at improving our understanding of how clouds of different structures form from a data-driven perspective: By predicting the visual appearance of cloud photographs from physical quantities obtained from reanalysis data and subsequently attributing the decisions to physical quantities using ``explainable AI'' methods, we try to identify relevant physical processes. At the current stage, this is just a proof of concept, being at least able to identify basic meteorologically plausible facts from data.