Floating Doppler Wind Lidar Motion Simulator for Horizontal Wind Speed Measurement Error Assessment

Salcedo-Bosch, Andreu; Farré-Guarné, Joan; Sala-Álvarez, Josep; Villares-Piera, Javier; Tanamachi, Robin L.; Rocadenbosch, Francesc

doi:10.1109/igarss47720.2021.9555023

Cited by 4 publications

(8 citation statements)

References 5 publications

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“…On the other hand, FDWLs suffer the influence of wave motion, which increases the variances of the reconstructed wind by the lidar [14][15][16]. However, within averaging periods typical of atmospheric measurements, i.e., 10 or 30 min, the error due to wave motion on the mean reconstructed wind vector are negligible (as they cancel out within such periods), as shown by multiple validation campaigns [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

et al. 2022

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This work presents a parametric-solver algorithm for estimating atmospheric stability and friction velocity from floating Doppler wind lidar (FDWL) observations close to the mast of IJmuiden in the North Sea. The focus of the study was two-fold: (i) to examine the sensitivity of the computational algorithm to the retrieved variables and derived stability classes (the latter through confusion-matrix theory), and (ii) to present data screening procedures for FDWLs and fixed reference instrumentation. The performance of the stability estimation algorithm was assessed with reference to wind speed and temperature observations from the mast. A fixed-to-mast Doppler wind lidar (DWL) was also available, which provides a reference for wind-speed observations free from sea-motion perturbations. When comparing FDWL- and mast-derived mean wind speeds, the obtained determination coefficient was as high as that of the fixed-to-mast DWL against the mast (ρ2=0.996) with a root mean square error (RMSE) of 0.25 m/s. From the 82-day measurement campaign at IJmuiden (10,833 10 min records), the parametric algorithm showed that the atmosphere was neutral (31% of the cases), stable (28%), or near-neutral stable (19%) during most of the campaign. These figures satisfactorily agree with values estimated from the mast measurements (31%, 27%, and 19%, respectively).

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Section: Introductionmentioning

confidence: 99%

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

et al. 2022

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“…The simulations performed by Bischoff et al (2015) emphasize the effect of wind shear in a nonuniform flow field but are not realistic enough to quantify the measurement error of real FLSs. The more recent study by Salcedo-Bosch et al (2021) gives a description of measurement error caused by motion in all 6 degrees of freedom and finds that it depends on the initial scan phase of the velocityazimuth display (VAD) scan. Unfortunately, they neither calculate the error based on the assumption of randomly distributed initial scan phase angles nor include the effect of wind shear in their model.…”

Section: Introductionmentioning

confidence: 99%

Quantification of motion-induced measurement error on floating lidar systems

Kelberlau

Mann

2022

Atmos. Meas. Tech.

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Abstract. Floating lidar systems (FLSs) are widely used for offshore wind site assessment, and their measurements show good agreement when compared to trusted reference sources. However, some influence of motion on mean wind speed data from FLS has to be assumed but could not have been quantified with experimental methods yet because the involved uncertainties are larger than the expected impact of motion. This study describes the motion-induced bias on horizontal mean wind speed estimates from FLS with the help of simulations of the lidar sampling pattern of a continuous-wave (CW) velocity–azimuth display (VAD) scanning wind lidar. Analytic modeling is used to validate the simulations. It is found that the mean bias depends on amplitude and frequency of motion, the angle between motion and wind direction, and wind speed and strength of wind shear. The simulations are used to quantify the measurement deviation that is caused by motion for the example of the Fugro SEAWATCH Wind LiDAR Buoy (SWLB) carrying a ZX 300M profiling wind lidar. The strongest bias of −0.67 % of the measurement values was estimated for a test case with “strong” waves aligned with the inflow wind direction. Under “normal” wave conditions the bias is smaller. The reason for these low errors lies in a fortunate combination of the frequencies of lidar prism rotation and tilt motion.

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“…This geometrical description permitted a preliminary study of the 6-DoF-motion-induced error in a LiDAR scan but was limited by the oversimplification of assuming a constant value for the initial scan phase. Departing from this geometrical description, Salcedo et al numerically simulated the 6 DoF CW DWL measurement error [37] and provided a first 6 DoF motion correction algorithm using an unscented Kalman filter [33]. The simulator [37] modeled each of the six DoF as a sinusoidal signal with a given amplitude, frequency, and phase and enabled full understanding of the motioninduced error in a LiDAR scan through the principle of error superposition.…”

Section: Introductionmentioning

confidence: 99%

“…Departing from this geometrical description, Salcedo et al numerically simulated the 6 DoF CW DWL measurement error [37] and provided a first 6 DoF motion correction algorithm using an unscented Kalman filter [33]. The simulator [37] modeled each of the six DoF as a sinusoidal signal with a given amplitude, frequency, and phase and enabled full understanding of the motioninduced error in a LiDAR scan through the principle of error superposition. Most of the CW FDWL motion-related error studies in the literature resorted to numerical simulations due to the inherent complexity of the velocity-azimuth display (VAD) algorithm, because it involves a least squares fit as a nonlinear operation.…”

Section: Introductionmentioning

confidence: 99%

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A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

et al. 2023

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This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating ZephIRTM 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed.

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Floating Doppler Wind Lidar Motion Simulator for Horizontal Wind Speed Measurement Error Assessment

Abstract: This study presents a wind retrieval simulator of a floating Doppler Wind Lidar (DWL) with 6 Degrees of Freedom (DoF) motion. The simulator considers a continuous-wave conical-scanning floating DWL which retrieves the wind vector from 50

Cited by 4 publications

References 5 publications

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

Quantification of motion-induced measurement error on floating lidar systems

A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

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