Taking the Motion out of Floating Lidar: Turbulence Intensity Estimates with a Continuous-Wave Wind Lidar

Abstract

Due to their motion, floating wind lidars overestimate turbulence intensity ( T I ) compared to fixed lidars. We show how the motion of a floating continuous-wave velocity–azimuth display (VAD) scanning lidar in all six degrees of freedom influences the T I estimates, and present a method to compensate for it. The approach presented here uses line-of-sight measurements of the lidar and high-frequency motion data. The compensation algorithm takes into account the changing radial velocity, scanning geometry, and measurement height of the lidar beam as the lidar moves and rotates. It also incorporates a strategy to synchronize lidar and motion data. We test this method with measurement data from a ZX300 mounted on a Fugro SEAWATCH Wind LiDAR Buoy deployed offshore and compare its T I estimates with and without motion compensation to measurements taken by a fixed land-based reference wind lidar of the same type located nearby. Results show that the T I values of the floating lidar without motion compensation are around 50 % higher than the reference values. The motion compensation algorithm detects the amount of motion-induced T I and removes it from the measurement data successfully. Motion compensation leads to good agreement between the T I estimates of floating and fixed lidar under all investigated wind conditions and sea states.

Assessment of a Coastal Offshore Wind Climate by Means of Mesoscale Model Simulations Considering High-Resolution Land Use and Sea Surface Temperature Data Sets

“…Offshore wind climate assessment is important to evaluate a prospective offshore wind project. The meteorological masts [1][2][3][4][5] and floating lidar [6,7] have been developed to observe the precise wind profile at the specific place. Meanwhile, mesoscale modeling is broadly used to evaluate the spatially distributed wind profile.…”

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

“…Therefore, there is a need to compensate for the effect of wave-induced motion on FDWL measurements [17,18]. Both the rotational motion (roll, pitch, and yaw) and translational motion (surge, sway, and heave) of the LiDAR induce errors in the retrieved HWS and WD [19,20]. The latter is of lesser concern because WD errors can easily be corrected by means of a reference compass installed on the buoy [21].…”

Estimation of Wave Period from Pitch and Roll of a Lidar Buoy

“…On the other hand, floating DWLs suffer wave-induced errors on wind measurements [ 8 ]. Sea waves induce translational (sway, surge, and heave for the x , y , and z axes, respectively) and rotational (roll, pitch, and yaw for the x , y , and z axes, respectively) motion to the floating DWL, which accounts for 6 degrees of freedom (DoF), creating a Doppler effect over the wind vector retrieval and turbulence intensity (TI) [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ], with errors of about 10% in horizontal wind speed (HWS). and 40% in TI [ 11 ].…”

“…The buoy’s design is a trade-off between accurate lidar wind measurements and attitude measurements for wave-induced motion compensation [ 16 ]. Of the 6 DoF of a wave buoy (sway, surge, heave, roll, pitch, and yaw), sway, surge, and yaw are mainly determined by wind and current forces, whereas heave, roll, and pitch are mainly determined by sea state [ 10 ] and are used to study sea waves [ 17 ]. Sea waves are a subject of interest in various fields such as marine engineering [ 18 ], oceanography [ 17 , 19 , 20 , 21 ], and wind engineering [ 22 , 23 ].…”