# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 5 Conclusions  
Using the model developed in this paper, we have investigated in detail the mechanisms responsible for the turbulence intensity measurement error of a floating LIDAR. The sea conditions influence the dynamic response of the buoy and consequently the turbulence intensity measurement of the FLS. In the search to elaborate the error of the LIDAR, we reviewed the most significant of the six degrees of freedom of motion on the LIDAR measurement behavior. Then, from the knowledge of the origin of the wind in the LIDAR frame of reference, we deduce the motions affecting the most the LIDAR measurement as well as the order of magnitude of the induced errors. On the WINDSEA_02 buoy in rough sea conditions, tilting and heaving are the most significant motions affecting the LIDAR measurement of turbulence intensity. This is due to the modification of the cone geometry, and the translation velocity appearing as an added component to the velocity vector, respectively. Figure 16: Effect of the model-based correction of the turbulence intensity measurement for the harshest week of the overall 13 (from 06/24/18 to 07/01/18). FLS measurements are compared with the fixed LIDAR measurements. All samples are normalized by the mean value from the fixed LIDAR. (**a**) Uncorrected floating LIDAR measurement. (**b**) Corrected measurement using the 10-min model-based correction presented in Section 3.1. During the experimental campaign in Fecamp, the motion-induced turbulence intensity measurement error of the floating LIDAR is an overestimate of 0.03 when compared to a cup anemometer (or a fixed LIDAR) over the 13 selected weeks. Based on a correlation study of the FLS error with the met-ocean conditions, we determined that the most correlated physical phenomenon with the FLS measurement error is the wave period, which is closely correlated with wave height and wind speed. However, no met-ocean conditions can ensure a zero error in the floating LIDAR turbulence intensity measurement for this specific buoy. Therefore, it is essential to correct the FLS TI measurement. The model-based method estimates the atmospheric turbulence intensity from 10-min floating LIDAR data and the six degrees of freedom time series of an IMU. Based on computationally efficient calculations relative to existing methods, it simulates the behavior of a profiler LIDAR and evaluates the impact of rotational and translational motions on the measured radial velocities. The method efficiently corrects the TI measurements of the floating LIDAR deployed at Fecamp for most of the 10-min estimates, allowing a satisfactory linear regression to be reached with respect to the fixed LIDAR measurements. The 10-min estimates that are still overestimated after using the model are generally isolated, allowing a peak filter to discard them. Further research should investigate sea-induced motions on buoys of different sizes and dynamic responses in order to gain a more comprehensive understanding of the relationship between sea conditions, buoy behavior and FLS turbulence intensity measurement errors. The model could be improved by a boundary layer shear, which would significantly complicate the matrix system. The model could also be adapted to the correction of CW LIDAR measurements. Conceptualization, T.D. ; data curation, T.D. ; formal analysis, T.D. ; funding acquisition, S.A.; investigation, S.A.; methodology, T.D. ; project administration, S.A.; software, T.D. ; supervision, S.A. and G.K.; visualization, T.D. ; writing--original draft, T.D. ; writing--review and editing, T.D., S.A., and G.K. All authors have read and agreed to the published version of the manuscript. This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region. We acknowledge EDF Renouvelable(r) and AKROCEAN(r) for providing the experimental data from the campaign of Fecamp.