An Innovative Decoupled Spatio-Vertical-Temporal Learning Model Based on Topography Guidance for Wind Vector...

Accurate wind speed forecasting (WSF) is pivotal for the secure grid integration of wind farms and the optimization of power dispatching. However, the stochastic nature of atmospheric dynamics and their complex interactions with local terrain pose significant challenges. Traditional spatiotemporal models inherently suffer from severe homogenization, irreversibly destroying fine-grained aerodynamic textures and leading to cumulative directional errors over extended horizons. To dismantle these limitations, this study proposes a novel Topography-Guided Spatio-Vertical-Temporal Transformer (TG-SVTFormer) for the multi-step forecasting of zonal and meridional wind vectors. Specifically, a dual-stage topography-guided mechanism explicitly injects static geographical variables (elevation, slope, and roughness) into both the spatial encoding and microscopic decoding phases, imposing key physical constraints on aerodynamic friction. Furthermore, the architecture employs a structured decoupled learning paradigm: a re-engineered Mix-Transformer spatial encoder deliberately eliminates deep-stage downsampling in favor of dilated convolutions-drastically expanding the global receptive field while preserving the intrinsic meteorological textures. Subsequently, the temporal evolution hub explicitly decouples the vertical dimension, utilizing upper-air atmospheric states to guide the target surface layer via simulated vertical momentum transport. The experiments showed the accuracy of TG-SVTFormer in predicting the wind speed with mean absolute error of 0.71 m/s in 3 h, 0.85 m/s in 6 h, and 1.06 m/s in 12 h. Compared to five existing baseline WSF models, the proposed TG-SVTFormer model demonstrates superior performance in wind speed forecasting and, more importantly, offers a transferable and physics-informed framework that can be adapted to other terrain-complex regions and extended to related renewable-energy and air-quality forecasting tasks.