用扩散模型融合观测与模拟数据，提升风力发电机高度风速的分辨率和精度。
Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
- 基于扩散模型与动态半径融合，将稀疏观测与模拟数据结合进行超分辨率重建。
- 相比传统方法，风速预测误差降低约20%，且在极端风况下表现更优。
- 适合风电选址、极端天气风险评估等需要高精度风场的应用场景。
机舱高度风速的高质量观测在空间和时间上都十分稀疏。尽管模拟数据在规则网格上广泛可用，但通常存在偏差且分辨率不足，难以支持风电场选址或基础设施尺度的极端天气风险评估（如阵风）。为充分利用两类数据，生成高分辨率、高质量的机舱高度风速（距地10米至约100米），本研究提出WindSR——一种融合数据同化的扩散模型，用于机舱高度风速的超分辨率降尺度。WindSR在降尺度过程中，利用先进的扩散模型整合稀疏观测数据与模拟场，引入动态半径融合方法，将观测与模拟结果有机结合，为扩散过程提供条件。训练与推理阶段均融入地形信息，以反映其对风场的关键影响。在卷积神经网络与生成对抗网络基线模型上的对比实验表明，WindSR在降尺度效率与精度方面均表现更优。数据同化使模型相对于独立观测的偏差降低约20%。
原文摘要 · Abstract (English)
High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse to inform wind-farm siting or to assess extreme-weather-related risks (e.g., gusts) at infrastructure scales. To fully utilize both data types for generating high-quality, high-resolution hub-height wind speeds (tens to ~100m above ground), this study introduces WindSR, a diffusion model with data assimilation for super-resolution downscaling of hub-height winds. WindSR integrates sparse observational data with simulation fields during downscaling using state-of-the-art diffusion models. A dynamic-radius blending method is introduced to merge observations with simulations, providing conditioning for the diffusion process. Terrain information is incorporated during both training and inference to account for its role as a key driver of winds. Evaluated against convolutional-neural-network and generative-adversarial-network baselines, WindSR outperforms them in both downscaling efficiency and accuracy. Our data assimilation reduces WindSR's model bias by approximately 20% relative to independent observations.
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