3 citations · 3 across the 10 of their papers we have counts for
10 papers
Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts
Mauricio Lima, Marika Koukoula, Romain Pilon +4
Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation. Downscaling with either dynamical or deep generative models can over…
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…
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…
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…
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…
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…
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
Antoine Leclerc, Erwan Koch, Monika Feldmann +2
Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive a…
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
Tom Beucler, Arthur Grundner, Sara Shamekh +3
The added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large de…
Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
Tom Beucler, Erwan Koch, Sven Kotlarski +3
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future.…