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arXiv · 2310.03499

IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

Kai Jeggle·Mikolaj Czerkawski·Federico Serva·Bertrand Le Saux·David Neubauer·Ulrike Lohmann
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Abstract

Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.

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Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, Ulrike Lohmann. 2023-10-05. IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision. https://arxiv.org/abs/2310.03499

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