[2604.03303] Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models

Physics > Atmospheric and Oceanic Physics arXiv:2604.03303 (physics) [Submitted on 30 Mar 2026] Title:Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models Authors:Joffrey Dumont Le Brazidec, Simon Lang, Martin Leutbecher, Baudouin Raoult, Gert Mertes, Florian Pinault, Aristofanis Tsiringakis, Pedro Maciel, Ana Prieto Nemesio, Jan Polster, Cathal O Brien, Matthew Chantry View a PDF of the paper titled Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models, by Joffrey Dumont Le Brazidec and 11 other authors View PDF HTML (experimental) Abstract:We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble forecasts into high-resolution ensembles by learning the conditional distribution of finer-scale residuals, defined as the difference between the high-resolution fields and the interpolated low-resolution inputs. The system is trained on reforecast pairs from ECMWF IFS, using coarse fields at 100 km to reconstruct fine-scale variability at 30 km resolution. The bulk of the training focuses on recovering small-scale structures, while fine-tuning in high-noise regimes enables the generation of extremes. Evaluation against the medium-range IFS ensemble target shows that the model increases probabilistic skill (FCRPS) for surface variables, reproduces target power spectra at small scales, captures physically cons...