A comprehensive procedure to process scanning lidar data for engineering wake model validation

We propose a comprehensive methodology to incorporate filtering, interpolation and uncertainties estimation in the processing of scanning wind lidar data. A full-scale wake measurement campaign has been carried out at an 8-MW prototype wind turbine in Bremerhaven, Germany, to apply and demonstrate the procedure. The filtering and interpolation of the scanning lidar data results in an average scan that fully covers the turbine rotor swept area. Once the filtered scans are processed, all observations are clustered in a capture matrix, where each bin can be ensemble-averaged according to wind direction, atmospheric stability and turbulence intensity. The final bin-averaged results were compared to an engineering wake model projected onto the lidar's beam directions, along with an uncertainty model which combines the contributions both from observations and simulation inputs. The results reveal the overall wake characteristics and the ability of the selected model to predict the wake under neutral conditions, with RMSE = 0.532 ms-1. Under stable conditions the model overestimates the wake deficit with greater RMSE = 1.108 ms-1. Nevertheless, we show that this post-processing methodology is effective and can be further applied in other long-range scanning lidar datasets, e.g., for offshore cluster wakes or blockage effect studies.