基于扩散模型、融合数据同化的轮毂高度风超分辨率重建
Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
AI总结：
针对轮毂高度风观测稀疏、模拟数据分辨率低有偏差的问题，提出融合数据同化的扩散模型WindSR，结合地形信息与动态半径混合方法实现超分辨率降尺度，效率精度优于基线模型，偏差降低约20%。
摘要：
轮毂高度风的高质量观测数据价值很高，但在时空维度上都十分稀疏。模拟数据可在规则网格上广泛获取，但通常存在偏差，且分辨率过粗，无法为风电场选址提供参考，也无法评估基础设施尺度下与极端天气相关的风险（如阵风）。为充分利用两类数据生成高质量、高分辨率的轮毂高度风速（地面以上数十米至约100米），本研究提出WindSR——一种结合数据同化的扩散模型，用于轮毂高度风的超分辨率降尺度。WindSR采用前沿扩散模型，在降尺度过程中将稀疏观测数据与模拟场融合。研究提出动态半径混合方法，用于合并观测与模拟数据，为扩散过程提供条件约束。地形信息作为风场的关键驱动因素，在训练和推理阶段均被纳入模型。与卷积神经网络和生成对抗网络基线模型相比，WindSR在降尺度效率和精度上均更优。相对于独立观测数据，本研究的数据同化方法将WindSR的模型偏差降低了约20%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
