Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
This work addresses the problem of probabilistic forecasting for specific regional weather, which is incremental as it adapts existing diffusion methods to a less-explored limited area context.
The authors tackled probabilistic limited area weather forecasting by introducing Diffusion-LAM, a model that uses conditional diffusion to generate forecasts within a defined area, showing accurate results on the MEPS dataset.
Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts primarily concentrated on deterministic predictions, the field has increasingly shifted toward probabilistic forecasting to better capture the forecast uncertainty. Most machine learning-based models have been designed for global-scale predictions, with only limited work targeting regional or limited area forecasting, which allows more specialized and flexible modeling for specific locations. This work introduces Diffusion-LAM, a probabilistic limited area weather model leveraging conditional diffusion. By conditioning on boundary data from surrounding regions, our approach generates forecasts within a defined area. Experimental results on the MEPS limited area dataset demonstrate the potential of Diffusion-LAM to deliver accurate probabilistic forecasts, highlighting its promise for limited-area weather prediction.