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

• URL: http://arxiv.org/abs/2310.03499v1
• Date: Thu, 5 Oct 2023 12:24:25 GMT
• Title: IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
• Authors: Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, and Ulrike Lohmann
• Abstract summary: 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-physical properties at geostationary satellite instruments and the quality of active satellite retrievals.
• Score: 26.970640961908032
• License: http://creativecommons.org/licenses/by/4.0/
• 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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