4 citations · 4 across the 2 of their papers we have counts for
3 papers
physics.ao-ph2024
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…
physics.ao-ph2023★ 4 cited
Super-resolved rainfall prediction with physics-aware deep learning
S. Moran, B. Demir, F. Serva +1
Rainfall prediction at the kilometre-scale up to a few hours in the future is key for planning and safety. But it is challenging given the complex influence of climate change on cl…
physics.ao-ph2023
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…