Understanding Wind Characteristics Over Different Terrains for Wind Turbine Deployment

Understanding how complex orography influences lower atmospheric winds is essential for accurately characterizing wind conditions, especially in regions considered for wind energy development. Complex terrain alters flow dynamics through mechanisms such as wind channeling, flow separation, and the formation of turbulent eddies and mountain waves, all of which significantly affect near-surface wind speed and direction. High-resolution numerical weather prediction (NWP) models, particularly the weather research and forecasting (WRF) model, have demonstrated substantial improvements in simulating these effects when fine-scale terrain and land surface datasets are employed, outperforming simulations based on coarse-resolution inputs. In this study, the WRF model is benchmarked for the first time using climate reanalysis data for the Askervein Hill campaign—a canonical field study of wind conditions over varying terrain. Multiple model configurations, including vertical and horizontal grid setups and ERA and NCEP/NCAR reanalysis input data, are evaluated to identify optimal settings for flat and complex terrain. Results show that while changes in vertical resolution have limited impact, finer horizontal resolution significantly improves predictions, particularly in complex orographic settings, with ERA data consistently outperforming NCEP/NCAR in all configurations. The model captures velocity profiles on flat terrain with RMSE within 2.5% (10–348 m heights) and turbulence intensity with RMSEs under 3%. Over complex terrain, near-surface flow is not adequately resolved, and the model overpredicts turbulence, which corresponds to an underprediction of the wind profile. However, the model performance improves significantly at wind turbine operational heights, with prediction errors reducing to below 2.4%. This discrepancy can be attributed to model limitations in resolving terrain-induced wind shear and stability gradients, to which the WRF model is particularly sensitive. These findings underscore the critical role of high-resolution terrain and land surface representation in improving WRF model performance for wind energy applications, highlighting the need for careful treatment of model physics, boundary conditions, and domain design to ensure accurate yet computationally efficient simulations.