Assessing the Impact of the Forest Canopy on Wind Speed, Vertical Wind Shear, and Turbulence across Wind Turbine Rotors

Wind turbine hub heights have been increasing in recent years, extending far above the surface layer to capture higher wind speeds. This is beneficial in forested regions, which have increased surface drag and therefore lower near-surface wind speeds. However, simple representations of surface conditions in models, such as aerodynamic surface roughness, do not accurately represent the magnitude or heterogeneity of the drag caused by forest canopies. As a result, the wind predicted by models over the rotor area of tall wind turbines may not provide reliable statistics of mean wind speed, vertical shear, and turbulence, which impacts the accuracy of power predictions. To improve the prediction of wind resources over heterogeneous forests, we adopt the canopy parameterization described in Arthur et al. (2019) to better represent realistic cases. We choose the NEON Wind River Experimental Forest site in southwest Washington, dominated by coniferous trees, as a test case since observational datasets from a tall meteorological tower, profiling lidar, and a scanning lidar were gathered by the project team during the late Summer and Fall of 2023. The canopy model is implemented in the Weather Research and Forecasting (WRF) model using a nested Large Eddy Simulation (LES) configuration to represent both the large-scale synoptic forcing and the diurnal cycle of boundary layer properties driven by local dynamics. Preliminary results show that the canopy model improves predictions of mean wind speed both within and above the canopy. In the near future, the same model configuration will be employed over a deciduous forest in the Appalachian Mountains, where a similar observational campaign is ongoing. The outcomes of the combined measurement and modeling analyses will be used to improve the performance of coarser operational mesoscale weather forecast models.