IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
This work addresses uncertainty in climate models for ice clouds, enabling improved process understanding and assessment of geoengineering methods, though it appears incremental as it applies existing methods to new data.
The authors tackled the problem of predicting ice microphysical properties in clouds by training a convolutional neural network on SEVIRI and DARDAR satellite data, achieving a new observational constraint with spatio-temporal coverage from geostationary satellites and quality from active retrievals.
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.