Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling

The development of 0.1◦ global weather forecasting modelsbased on machine learning (ML) is constrained by the limited availabil-ity of high-resolution data, as decades of reanalysis are only availableat 0.25◦ resolution. While existing approaches fine-tune 0.25◦ forecastmodels on limited 0.1◦ samples, we show that this transfer is hinderedby the irreversible information loss inherent in coarse-resolution fore-casting. Therefore, we propose BaguanHR, a framework that shiftsthe focus from transferring models to transferring data. We first showthat super-resolution (SR) has lower conditional entropy and input am-plification than forecasting, making it a more robust vehicle for reso-lution transfer. By leveraging this advantage through variable-wise SR,we synthesize extensive 0.1◦ data from ERA5. BaguanHR’s performanceon the synthetic-plus-real dataset exceeds both ML-based methods andIFS-HRES, achieving superior performance across over 85% of the leadtimes within 72 hours. Furthermore, our findings highlight a power-lawscaling effect, as a twofold increase in data reduces RMSE by 4.6% for 72-hour forecasting and 4.9% for 120-hour forecasting. Our results demon-strate that scaling high-resolution ML-based forecasting is primarily adata bottleneck, and that variable-wise super-resolution provides a sim-ple yet general solution to unlock long coarse-resolution reanalyses forhigh-resolution training.