| 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 |
| date | 2026-07-31 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.14652 |
| affiliation | NJUHML |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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. |
cite
@misc{zhao2026pushing,
title={Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling},
author={Yang Zhao and Peisong Niu and Tian Zhou and Ziqing Ma and Guanlong Ma and Rong Jin and Huiling Yuan and Liang Sun},
year={2026},
eprint={2608.14652},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.14652}
}