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

[2608.14652] Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14652 (cs) [Submitted on 31 Jul 2026] Title: Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling Authors: Yang Zhao , Peisong Niu , Tian Zhou , Ziqing Ma , Guanlong Ma , Rong Jin , Huiling Yuan , Liang Sun View a PDF of the paper titled Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling, by Yang Zhao and 7 other authors View PDF HTML (experimental) Abstract: The development of 0.1$^{\circ}$ global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25$^{\circ}$ resolution. While existing approaches fine-tune 0.25$^{\circ}$ forecast models on limited 0.1$^{\circ}$ samples, we show that this transfer is hindered by the irreversible information loss inherent in coarse-resolution forecasting. Therefore, we propose BaguanHR, a framework that shifts the focus from transferring models to transferring data. We first show that super-resolution (SR) has lower conditional entropy and input amplification than forecasting, making it a more robust vehicle for resolution transfer. By leveraging this advantage through variable-wise SR, we synthesize extensive 0.1$^{\circ}$ data from ERA5. BaguanHR's performance on the synthetic-plus-real dataset exceeds both ML-based methods and IFS-HRES, achieving superior performance across over 85% of the lead times within 72 hours. Furthermore, our findings highlight a power-law scaling 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 demonstrate that scaling high resolution ML-based forecasting is primarily a data bottleneck, and that variable-wise super-resolution provides a simple yet general solution to unlock long coarse-resolution reanalyses for high-resolution training. Comments: Accepted by ECCV2026 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14652 [cs.LG] (or arXiv:2608.14652v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14652 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Peisong Niu [ view email ] [v1] Fri, 31 Jul 2026 03:43:21 UTC (46,536 KB) Full-text links: Access Paper: View a PDF of the paper titled Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling, by Yang Zhao and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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