Urban wind flow reconstruction is essential for assessing air quality, heat dispersion, and pedestrian comfort, yet remains challenging when only sparse sensor data are available. We propose GenDA, a generative data assimilation framework that reconstructs high-resolution wind fields on unstructured meshes from limited observations. The model employs a multiscale graph-based diffusion architecture trained on computational fluid dynamics (CFD) simulations and interprets classifier-free guidance as a learned posterior reconstruction mechanism: the unconditional branch learns a geometry-aware flow prior, while the sensor-conditioned branch injects observational constraints during sampling. This formulation enables obstacle-aware reconstruction and generalization across unseen geometries, wind directions, and mesh resolutions without retraining. We consider both sparse fixed sensors and trajectory-based observations using the same reconstruction procedure. When evaluated against supervised graph neural network (GNN) baselines and classical reduced-order data assimilation methods, GenDA reduces the relative root-mean-square error (RRMSE) by 25-57% and increases the structural similarity index (SSIM) by 23-33% across the tested meshes. Experiments are conducted on Reynolds-averaged Navier-Stokes (RANS) simulations of a real urban neighbourhood in Bristol, United Kingdom, at a characteristic Reynolds number of $\mathrm{Re}\approx2\times10^{7}$, featuring complex building geometry and irregular terrain. The proposed framework provides a scalable path toward generative, geometry-aware data assimilation for environmental monitoring in complex domains.

翻译：城市风场重建对于评估空气质量、热量扩散及行人舒适度至关重要，但在仅有稀疏传感器数据时仍具挑战性。本文提出GenDA，一种生成式数据同化框架，能够从有限观测数据中重建非结构化网格上的高分辨率风场。该模型采用基于多尺度图结构的扩散架构，在计算流体动力学（CFD）模拟数据上进行训练，并将无分类器引导解释为一种学习到的后验重建机制：无条件分支学习几何感知的流场先验，而传感器条件分支在采样过程中注入观测约束。此公式化方法实现了障碍物感知的重建，并能够泛化至未见过的几何形状、风向和网格分辨率，且无需重新训练。我们采用相同的重建流程处理稀疏固定传感器和基于轨迹的观测数据。与监督式图神经网络（GNN）基线方法及经典降阶数据同化方法相比，GenDA在所有测试网格上将相对均方根误差（RRMSE）降低了25-57%，并将结构相似性指数（SSIM）提高了23-33%。实验基于英国布里斯托尔一个真实城市街区的雷诺平均纳维-斯托克斯（RANS）模拟进行，其特征雷诺数为$\mathrm{Re}\approx2\times10^{7}$，包含复杂的建筑几何形状和不规则地形。所提出的框架为复杂领域的环境监测提供了一条可扩展的、生成式且几何感知的数据同化路径。