TerraWind: A Deep Learning-Based Near-Surface Winds Downscaling Model for Complex Terrain Region

Abstract

Wind downscaling is crucial for refining coarse-scale wind estimates, improving local-scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter-variable and inter-station spatial correlations. Addressing this, we propose TerraWind, a deep learning-based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter-station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6 (Formula presented.) and 33.3 (Formula presented.), respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3 (Formula presented.) in wind speed MAE and 25.6 (Formula presented.) in wind speed RMSE compared to four deep learning models (Wind-Topo, DeepCAMS, RCM-emulator, and Uformer).