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.
@article{arxiv.2310.03499,
title = {IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision},
author = {Kai Jeggle and Mikolaj Czerkawski and Federico Serva and Bertrand Le Saux and David Neubauer and Ulrike Lohmann},
journal= {arXiv preprint arXiv:2310.03499},
year = {2023}
}
Comments
A Preprint. Submitted to Tackling Climate Change with Machine Learning: workshop at NeurIPS 2023