Reconstructing fine-scale 3D wind fields with terrain-informed machine learning

Fine-scale near-surface wind field prediction is essential for a wide range of applications. However, most operational and AI-based weather models operate at kilometer-scale resolution, where terrain-induced wind features such as slope jets, flow deflection, and recirculation are systematically averaged out. Here we introduce FuXi-CFD, a machine learning-based framework designed to generate detailed three-dimensional (3D) near-surface wind fields at 30-meter horizontal resolution, using only coarse-resolution atmospheric inputs and high-resolution terrain information. The model is trained on a large-scale dataset generated via computational fluid dynamics (CFD), encompassing a wide range of terrain types and inflow conditions. Although relying only on horizontal wind inputs, FuXi-CFD infers the full 3D wind fields-including latent variables such as vertical velocity and turbulence-related features. It achieves CFD-comparable accuracy while reducing inference time from hours to seconds. Notably, the model also generalizes well to real-world conditions, as demonstrated by consistent performance against independent wind-tower observations. This capability enables real-time wind field reconstruction for terrain-sensitive applications such as wind turbine siting, power forecasting, and wildfire spread modeling.