Contributed to this science blog, which discusses the advancements and potential of machine learning (ML) in weather forecasting.
While large language and image models have gained attention, ML models for weather forecasting have been making significant progress.
The blog post highlights ECMWF's exploration of ML-based weather forecasting, starting with using reanalysis data to predict geopotential height. Initially, ML-based models had skill comparable to coarse-resolution simulations, making operational use unlikely.
However, in recent years, ML-based weather forecasts have rapidly improved, matching and even surpassing the scores of traditional numerical models. ECMWF has implemented infrastructure to evaluate these models, and they have shown promising results when compared to analyses and observations.
Despite the progress, we emphasize the need to ensure physically consistent and meteorologically meaningful forecasts.
We conclude by stating that ML and physical models can work together in a hybrid symbiotic approach.
ECMWF aims to continue exploring this integration to further enhance weather forecasts.
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