Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models
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 aga...
| Authors | Joffrey Dumont Le Brazidec, Simon Lang, Martin Leutbecher et al. |
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| Primary category | physics.ao-ph |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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