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
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.