3 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
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…