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

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