67 citations · 208 across the 7 of their papers we have counts for
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physics.ao-ph2022★ 16 cited
Machine-learned cloud classes from satellite data for process-oriented climate model evaluation
A. Kaps, A. Lauer, G. Camps-Valls +3
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks…
physics.ao-ph2022★ 34 cited
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…