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Poster
in
Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

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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[ Poster] 
 
presentation: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems
Sat 16 Dec 6:15 a.m. PST — 3:30 p.m. PST

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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