AI 中文总结
针对复杂地形近地表风预测难题，开发KiloGen扩散后验采样框架，通过学习WRF模型先验并结合ECMWF预报约束后验采样，在山西应用效果良好，降低风速均方根误差，为千米尺度风预报增强提供有效途径。
AI 中文摘要
复杂地形近地表风的可靠预测受业务预报空间分辨率与地形控制的局部风变率不匹配限制。本文开发了KiloGen，一种用于增强千米尺度风预报的扩散后验采样框架。它从WRF模型模拟中学习高分辨率矢量风先验，并在推理时用欧洲中期天气预报中心25公里的预报约束后验采样。在中国山西复杂山区应用时，KiloGen重建了地形组织的风结构，恢复了高波数变率，同时保留了业务预报的大尺度演变。站点验证表明，KiloGen在评估产品中总体风速均方根误差最低，在高处和地形复杂站点优势更大，强风条件下改善最明显。结果表明扩散后验采样为地形感知千米尺度风预报增强提供了有效方法。
英文摘要
Reliable prediction of near surface wind over complex terrain is limited by the mismatch between the spatial resolution of operational forecasts and terrain controlled local wind variability. Here, we develop KiloGen, a diffusion posterior sampling framework for kilometre scale wind forecast enhancement. KiloGen learns a high resolution vector wind prior from Weather Research and Forecasting (WRF) model simulations and constrains posterior sampling with 25 km forecasts from the European Centre for Medium Range Weather Forecasts (ECMWF) at inference time. This formulation avoids paired ECMWF and WRF training samples and an explicitly learned mapping from coarse to fine resolution. Applied over Shanxi, China, a region with complex mountainous terrain, KiloGen reconstructs terrain organized wind structures and restores high wavenumber variability while retaining the large scale evolution of the operational forecast. Station verification shows that KiloGen achieves the lowest overall wind speed root mean square error (RMSE) among the evaluated products, with larger benefits at elevated and topographically complex sites. The improvement is strongest under strong wind conditions, reducing RMSE by approximately 10% for observed winds above 20 m s^-1. Across 13 distinct strong wind events, KiloGen improves upon the 0.25 degree ECMWF forecast in all cases and outperforms the 0.1 degree ECMWF forecast in most cases. These results show that diffusion posterior sampling provides an effective approach for terrain aware kilometre scale wind forecast enhancement.
CommentsMain manuscript with 7 figures and Supplementary Information with 5 supplementary figures
