3 papers
physics.ao-ph2026
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
Epistemic and Aleatoric Uncertainty Quantification in Weather and Climate Models
Laura A. Mansfield, Hannah M. Christensen
Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for di…
physics.ao-ph2024
Generative Diffusion-based Downscaling for Climate
Robbie A. Watt, Laura A. Mansfield
Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate mode…