14 citations · 34 across the 16 of their papers we have counts for
8 papers · 1 filter
From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Ellen Sarauer, Mierk Schwabe, Philipp Weiss +3
The representation of cloud microphysics and its nonlinear character and scale-dependence is a remaining source of uncertainty in Earth system models (ESMs). Here, we develop and c…
Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
Elias Haslauer, Mierk Schwabe, Andreas Dörnbrack +4
State-of-the-art Earth system models (ESMs) cannot explicitly resolve many small-scale atmospheric processes such as atmospheric gravity waves, and thus must represent, or paramete…
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
Kevin Debeire, Aytaç Paçal, Pierre Gentine +3
Regional climate information at kilometer scales is essential for assessing the impacts of climate change, but generating it with global climate models is too expensive due to thei…
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Katharina Hafner, Sara Shamekh, Guillaume Bertoli +4
Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributi…
Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
Helge Heuer, Tom Beucler, Mierk Schwabe +3
Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence.…
Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning
Arthur Grundner, Tom Beucler, Julien Savre +3
Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-dri…