14 citations · 31 across the 11 of their papers we have counts for
5 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…
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.…
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
Gunnar Behrens, Tom Beucler, Fernando Iglesias-Suarez +5
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here…
Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON
Helge Heuer, Mierk Schwabe, Pierre Gentine +2
Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computat…