用全球-区域协同模型实现5公里级精准天气预报
Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
- 通过双向耦合模块动态融合全球与区域气象特征
- 在0.05°（约5km）分辨率下显著优于传统预报方法
- 擅长捕捉地形风和焚风等精细气象现象，适合气象科研与防灾
数据驱动的天气模型在全局中程预报方面已取得进展，但高分辨率区域预报仍面临挑战，主要源于大尺度动力与小尺度过程（如地形环流、海岸效应）之间多尺度相互作用未被充分解析。本文提出一种全球-区域协同框架，通过新型双向耦合模块ScaleMixer，将预训练的Transformer全球模型与高分辨率区域网络协同结合。ScaleMixer利用自适应关键位置采样识别气象关键区域，并通过专用注意力机制实现跨尺度特征交互。该框架在中国区域实现了0.05°（约5公里）空间分辨率和1小时时间分辨率的预报，显著优于业务数值预报（NWP）和现有AI基准模型，在网格再分析数据及实时气象站观测上均表现优异。其在捕捉地形风模式与焚风增温等细粒度现象方面表现突出，兼具全局一致性与高分辨率保真度。代码已公开于https://anonymous.4open.science/r/ScaleMixer-6B66。
原文摘要 · Abstract (English)
Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions between large-scale dynamics and small-scale processes such as terrain-induced circulations and coastal effects. This paper presents a global-regional coupling framework for kilometer-scale regional weather forecasting that synergistically couples a pretrained Transformer-based global model with a high-resolution regional network via a novel bidirectional coupling module, ScaleMixer. ScaleMixer dynamically identifies meteorologically critical regions through adaptive key-position sampling and enables cross-scale feature interaction through dedicated attention mechanisms. The framework produces forecasts at $0.05^\circ$ ($\sim 5 \mathrm{km}$ ) and 1-hour resolution over China, significantly outperforming operational NWP and AI baselines on both gridded reanalysis data and real-time weather station observations. It exhibits exceptional skill in capturing fine-grained phenomena such as orographic wind patterns and Foehn warming, demonstrating effective global-scale coherence with high-resolution fidelity. The code is available at https://anonymous.4open.science/r/ScaleMixer-6B66.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。