A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling

Abstract High‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in deep learning algorithms results in the inability to accurately reconstruct fine‐scale structures after downscaling. In this study, we propose a Terrain‐Constraint Wind Downscaling Model (TCWDM), a lightweight deep learning model consisting of a downscaling module and a terrain‐constraint module. By combining low‐resolution wind with high‐resolution terrain data, the model achieves a tenfold downscaling of spatial wind fields and reconstructs the detailed structure of the wind. Due to the incorporation of an attention mechanism, multi‐feature inputs, and the terrain‐constraint module, TCWDM demonstrates superior downscaling performance. Compared to traditional interpolation methods and other deep learning models, the mean absolute error is reduced by up to 49%. The terrain‐constraint module, in particular, contributes most significantly to the model's performance, especially in complex terrains, where it enables greater optimization of downscaling results. Furthermore, due to the lightweight model structure and a specific fine‐tuning strategy, TCWDM can deliver significantly better downscaling results at a lower cost across different regions, offering potential for broader applications.