Evaluating the Impact of Motion Compensation on Turbulence Intensity Measurements from Continuous-Wave and Pulsed Floating Lidars

Abstract. Floating Lidar Systems (FLS) play a crucial role in offshore wind resource assessment, offering a cost-effective and flexible alternative to traditional meteorological masts. While wind speed and direction measurements from FLS demonstrate high accuracy without further in-depth correction required, platform motions introduce systematic overestimation of turbulence intensity (TI). This motion-induced bias requires compensation techniques to ensure reliable TI measurements. This study evaluates the impact of a deterministic motion compensation algorithm on TI measurements from two FLS of the same type, equipped with different lidar types: a continuous-wave (cw) lidar and a pulsed lidar. The analysis compares raw and motion-compensated TI data against reference measurements from a fixed cw lidar and a met mast cup anemometer.

A comprehensive evaluation is conducted using multiple performance metrics, including Regression Analysis, Mean Bias Error (MBE), Mean Relative Bias Error (MRBE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Representative TI Error, and Quantile-based distribution analysis. The results show that the applied motion compensation significantly reduces the overestimation of TI, with the pulsed lidar exhibiting the most substantial relative improvement across various metrics. The cw lidar, while also benefiting from motion compensation, demonstrates a closer alignment with the fixed lidar in terms of absolute bias reduction.

Despite these improvements, residual discrepancies remain, attributed to differences in measurement principles, remaining motion effects, lidar-specific characteristics and sensitivities. Our findings confirm that deterministic motion compensation can enhance the reliability of FLS-derived TI measurements, bringing them closer to those obtained from a fixed lidar system. Future work should focus on refining compensation algorithms by incorporating lidar-specific sensitivities, improving sensor time synchronization, and exploring machine learning-based enhancements for an even better agreement with a met mast reference.