11 papers
BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling
Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat +1
Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical ocea…
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2
Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach to separating the internal variability…
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…
Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching
Aymeric Delefosse, Anastase Charantonis, Dominique Béréziat
Machine learning-based weather forecasting models now surpass state-of-the-art numerical weather prediction systems, but training and operating these models at high spatial resolut…
STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
David Landry, Isabelle Gouttevin, Hugo Merizen +2
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gri…
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Julie Keisler, Anastase Alexandre Charantonis, Yannig Goude +2
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying str…