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cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

Quantile Regression, Variational Autoencoders, and Diffusion Models for Uncertainty Quantification: A Spatial Analysis of Sub-seasonal Wind Speed Prediction

Ganglin Tian, Anastase Alexandre Charantonis, Camille Le Coz +2

This study aims to improve the spatial representation of uncertainties when regressing surface wind speeds from large-scale atmospheric predictors for sub-seasonal forecasting. Sub…

cs.LG2025

Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model

Ganglin Tian, Camille Le Coz, Anastase Alexandre Charantonis +3

Sub-seasonal wind speed forecasts provide valuable guidance for wind power system planning and operations, yet the forecast skills of surface winds decrease sharply after two weeks…

cs.LG2024

ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting

Guillaume Couairon, Renu Singh, Anastase Charantonis +2

Weather forecasting plays a vital role in today's society, from agriculture and logistics to predicting the output of renewable energies, and preparing for extreme weather events.…