3 papers
physics.ao-ph2025
Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via a $50,000 Kaggle Competition
Jerry Lin, Zeyuan Hu, Tom Beucler +24
Subgrid machine-learning (ML) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without…
physics.ao-ph2025
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.…
physics.ao-ph2023
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…