KiloDA：从稀疏站点观测重建千米尺度近地面风场
KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations
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中文总结 AI 辅助
KiloDA利用扩散模型从历史WRF预报中学习风场结构，仅用稀疏站点观测重建千米尺度近地面风，在留出区域将风速RMSE较ERA5降低19%。
中文摘要 AI 辅助
准确的千米尺度近地面风对于理解复杂地形上的大气过程至关重要，然而从稀疏且分布不均的观测中重建这些风场仍然困难。在此，我们介绍KiloDA，一个用于从地面站点进行逐小时千米尺度风重建的扩散框架。KiloDA从历史3公里天气研究与预报（WRF）模型预报中学习风场的统计分布和空间结构。在每次重建时刻，不使用同期WRF场。相反，站点观测提供了对当前大气状态的唯一约束，并引导从学习到的先验中进行后验采样。在理想化的WRF实验中，当仅观测到0.24%的网格单元时，KiloDA能够恢复局部风结构，并且在不同地形条件和风速范围内，相比传统插值方法展现出整体优势。这种能力在很大程度上可迁移到真实观测中。在一个完全留出的区域内，仅使用区域外的观测，KiloDA将中值风速均方根误差（RMSE）相对于ERA5再分析数据降低了19%，其中在高海拔和高起伏地形上的改进最为显著。随机站点留出测试进一步证实，这一优势在不同复杂地形位置和留出配置中均成立。这些结果表明，历史模型档案可以提供有用的结构知识，用于从稀疏观测重建千米尺度风场，而无需对当前大气状态进行精确的模型估计。
英文摘要
Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.
发表机构
- Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）
- China Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所地球系统数值模拟与应用重点实验室）
- University of Chinese Academy of Sciences（中国科学院大学）
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