ScaleCast框架实现公里级天气预测
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment
ScaleCast框架让全球天气预报模型能指导高分辨率区域预测，支持多种全球驱动器，无需重新训练。
ScaleCast框架通过全球-区域对齐方法解决公里级区域天气预报问题。该模型在ERA5全球分析和CERRA区域再分析数据上测试，表面和高层大气变量预测均有提升。单个训练模型支持Pangu-Weather、GraphCast和HRES多种全球预报驱动器，无需重新训练。在HRRR 3公里分辨率数据上的微调展示了框架对不同区域和空间分辨率的适应性。
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment
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.
