Generating ensembles of spatially-coherent in-situ forecasts using flow matching
This work addresses the need for low-cost, efficient weather forecasting systems by providing a method that generates ensembles from limited simulations, though it is incremental as it builds on existing flow matching and transformer techniques.
The authors tackled the problem of generating spatially-coherent and multivariate in-situ weather forecasts by proposing FMAP, a flow matching-based postprocessing method that improves marginal performance at stations and better represents observation correlation structures, as demonstrated on the EUPPBench dataset for up to five-day lead times.
We propose a machine-learning-based methodology for in-situ weather forecast postprocessing that is both spatially coherent and multivariate. Compared to previous work, our Flow MAtching Postprocessing (FMAP) better represents the correlation structures of the observations distribution, while also improving marginal performance at the stations. FMAP generates forecasts that are not bound to what is already modeled by the underlying gridded prediction and can infer new correlation structures from data. The resulting model can generate an arbitrary number of forecasts from a limited number of numerical simulations, allowing for low-cost forecasting systems. A single training is sufficient to perform postprocessing at multiple lead times, in contrast with other methods which use multiple trained networks at generation time. This work details our methodology, including a spatial attention transformer backbone trained within a flow matching generative modeling framework. FMAP shows promising performance in experiments on the EUPPBench dataset, forecasting surface temperature and wind gust values at station locations in western Europe up to five-day lead times.