8 papers
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
Dan Assouline, Erwan Koch, Federico Amato +5
Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that gl…
Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
Savannah L. Ferretti, Jerry Lin, Sara Shamekh +3
Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly no…
A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
Max Defez, Filippo Quarenghi, Mathieu Vrac +2
Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal m…
Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss
Filippo Quarenghi, Ryan Cotsakis, Tom Beucler
The ``differentiability gap'' presents a primary bottleneck in Earth system deep learning: since models cannot be trained directly on non-differentiable scientific metrics and must…
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
Louise Largeau, Tom Beucler, David Leutwyler +3
The coarse spatial resolution of gridded climate models, such as general circulation models, limits their direct use in projecting socially relevant variables like extreme precipit…
Global Forecasting of Tropical Cyclone Intensity Using Neural Weather Models
Milton Gomez, Louis Poulain--Auzeau, Alexis Berne +1
Numerical Weather Prediction (NWP) models that integrate coupled physical equations forward in time are the traditional tools for simulating atmospheric processes and forecasting w…