Do we really need bigger AI weather forecasting models to improve resolution?

Researchers demonstrate that a relatively small machine-learning downscaler can enhance coarse global AI weather forecasts without building substantially larger forecasting models. Trained on ERA5 and CERRA reanalysis data, their approximately 2-million-parameter U-Net uses terrain, coastline, land fraction, location, season, time of day, and random noise to convert 0.25° AIFS-ENS temperature forecasts over France, roughly 28 km resolution, into 5.5 km probabilistic ensemble forecasts in seconds. Against Météo-France station observations, the downscaled forecasts reduced RMSE by 14% and CRPS by 21% during the first two days, with smaller but sustained gains over ten days, particularly in mountainous and coastal regions. The authors emphasize that downscaling can add predictable local detail but cannot correct broad errors in the original forecast, reduce large-scale uncertainty, or exceed the quality of its fine-resolution training data. They present the work as a demonstration rather than a comprehensive evaluation and make the model, code, tutorial, and interactive demo available while proposing future work on variables such as precipitation and wind, temporal downscaling, and direct training on station observations.

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