5 papers
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…
Opportunities and challenges of quantum computing for climate modelling
Mierk Schwabe, Lorenzo Pastori, Inés de Vega +6
Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This…
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…
Simulating the Air Quality Impact of Prescribed Fires Using Graph Neural Network-Based PM Forecasts
Kyleen Liao, Jatan Buch, Kara Lamb +1
The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM concentrations in recent years. In a changing climate,…