Diffusion downscaling for regional convective-scale weather prediction

Currently, operational weather forecasts rely on physically-based modeling approaches, with Numerical Weather Prediction (NWP) models used to determine atmospheric conditions over the coming hours to days. However, the configuration of NWP models is strongly constrained by computational resources, which notably limits, for instance, their horizontal resolution. Current operational systems typically run at resolutions of around 10 km at the global scale and, at best, around 1 km at the regional scale. A promising alternative to explicitly increasing resolution is statistical downscaling, which consists of learning the relationship between large-scale and fine-scale forecasts. This task, similar to super-resolution, can leverage recent advances in AI for computer vision.

The literature on downscaling approaches for weather and climate prediction is already extensive, with a wide range of AI methods proposed, from standard convolutional neural networks to more advanced generative approaches, including GANs and diffusion models. Generative methods learn a probabilistic representation of the data, which helps avoid the fine-scale blurring commonly encountered in standard AI approaches and naturally enables the generation of ensemble forecasts. However, most existing applications for weather or climate downscaling focus on a limited set of variables or treat each variable independently.

In this work, we develop a diffusion-based downscaling model, termed AROME-DS, to emulate high-resolution forecasts from the French regional model AROME (0.025°) from those of the French global model ARPEGE (0.1°). The model is based on a graph transformer encoder–processor–decoder architecture implemented within the Anemoi framework. It is trained on five years of hourly analyses produced by the French operational services at Météo-France. AROME-DS jointly predicts 70 atmospheric variables, including 11 vertical levels and multiple surface fields such as near-surface temperature, precipitation, and wind gusts, representing a significant increase in variable dimensionality compared to existing AI-based downscaling approaches.

We show that AROME-DS produces realistic high-resolution forecasts and successfully retrieves fine-scale features related to orography. We further investigate how ensemble forecasts obtained by sampling the distribution learned by the diffusion model can be used to represent uncertainty in specific weather situations. Finally, we compare this downscaling approach with an AI-based autoregressive regional NWP model, providing insights onto the best way to leverage AI in operational weather prediction.