1 citations · 1 across the 2 of their papers we have counts for
3 papers · 1 filter
IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI
Kai Jeggle, Mikolaj Czerkawski, Federico Serva +3
IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\t…
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
Kai Jeggle, Mikolaj Czerkawski, Federico Serva +3
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 w…
Understanding cirrus clouds using explainable machine learning
Kai Jeggle, David Neubauer, Gustau Camps-Valls +1
Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This…