发表机构
Tsinghua University; The Chinese University of Hong Kong; Sun Yat-Sen University（清华大学; 香港中文大学; 中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出ScaleCast框架，通过全球-区域对齐解决公里级区域预报的耦合挑战，实验表明其可改进多变量预报，适配不同区域域与分辨率，支持多全球预报驱动源且表现更优。
AI 中文摘要
公里级区域天气预报对本地气象预警和气象敏感决策至关重要。现有数据驱动方法常依赖数值预报获取大尺度指导，或需额外训练全球预报组件。预训练的全球气象模型提供了高效的大尺度预报源，促使人们复用其指导高分辨率区域预报。但这种耦合需对齐不同网格的全球与区域表示，并将全球指导与局地交互整合以推进区域状态。我们提出ScaleCast，一种区域预报框架，通过全球-区域对齐解决上述挑战。其全球-区域转换模块将联合全球与区域表示与区域位置对齐，而全球-区域对齐与动力学模块将对齐后的指导与区域邻域交互结合。使用0.25度网格的ERA5全球分析和5.5公里间距的CERRA区域再分析的实验表明，该框架在地表和高空变量的区域预报上有所改进，单个训练模型可支持多个全球预报驱动源（即Pangu-Weather、GraphCast和HRES）而无需特定重训练。对3公里间距的HRRR进行微调进一步证明了该框架对不同区域域和空间分辨率的适应性。风暴案例研究显示气旋定位和中心气压估计有所改进，与HadISD站点观测的比较显示其与局地温度和湿度变化的一致性更紧密。
英文摘要
Kilometer-scale regional weather forecasting is essential for local weather warnings and weather-sensitive decisions. Existing data-driven approaches often rely on numerical forecasts for large-scale guidance or require additional training of global forecasting components. Pretrained global weather models offer an efficient source of large-scale forecasts, motivating their reuse to guide high-resolution regional prediction. However, this coupling requires aligning global and regional representations across different grids and integrating global guidance with local interactions to advance regional states. We propose ScaleCast, a regional forecasting framework that addresses these challenges through Global-Regional Alignment. Its Global-Regional Conversion module aligns joint global and regional representations with regional locations, while the Global-Regional Alignment and Dynamics block combines aligned guidance with regional neighborhood interactions. Experiments using ERA5 global analyses on a 0.25-degree grid and CERRA regional reanalysis at 5.5 km spacing demonstrate improved regional forecasts across surface and upper-air variables, with a single trained model supporting multiple global forecast drivers (i.e., Pangu-Weather, GraphCast, and HRES) without specific retraining. Fine-tuning on HRRR at 3 km spacing further demonstrates the framework's adaptability to a different regional domain and spatial resolution. Windstorm case studies show improved cyclone positioning and core-pressure estimates, while comparisons with HadISD station observations show closer agreement with local temperature and humidity changes.
