2 citations · 3 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…