Performance evaluation of a floating lidar buoy in nearshore conditions

Gutierrez-Antunano, M. A.; Tiana-Alsina, Jordi; Rocadenbosch, Francesc

doi:10.1002/we.2118

Cited by 15 publications

(32 citation statements)

References 20 publications

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“…Figure 6 shows that the motion-uncorrected mean difference, MD re f , had a positive bias of 0.13 m/s when using the fixed lidar as reference. This bias accounts for the systematic error in the measured HWS standard deviation caused by floating lidar motion as previously reported in [27,59]. After motion correction, the mean difference MD corr reduced to the virtually unbiased figure of −0.03 m/s when using the fixed lidar as reference.…”

Section: Analysis Of the Whole Campaignsupporting

confidence: 68%

Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar

et al. 2018

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This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to −0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected).

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Section: Analysis Of the Whole Campaignsupporting

confidence: 68%

Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar

et al. 2018

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“…Although the manuscript uses the STD as a proxy of the TI, the apparent TI follows and magnifies this trend (on account of the fact that TI is defined as the ratio of the STD HWS to the HWS). Although comparative results between EOLOS lidar buoy and reference met mast HWS measurements proved the fulfillment of all requirements for commercial acceptance of floating of our lidar systems [11], Doppler Wind Lidar offshore operation needs further improvement regarding the TI assessment.…”

Section: Discussionmentioning

confidence: 90%

Floating Doppler Wind Lidar Measurement of Wind Turbulence: A Cluster Analysis

Salcedo-Bosch

Gutierrez-Antunano

Tiana-Alsina

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an offshore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.).

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“…Incipient studies addressed the topic as follows: in [ 25 ], the wave-induced buoy’s tilt period was computed from the smoothed fast Fourier transform (FFT) of pitch and roll time series. The most prominent peak of these 2 FFTs was chosen as the most relevant spectral component, and the period was estimated as the inverse of the frequency corresponding to it.…”

Section: Introductionmentioning

confidence: 99%

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

Salcedo-Bosch

Rocadenbosch

Gutierrez-Antunano

et al. 2021

Sensors

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This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, ρ = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.

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Performance evaluation of a floating lidar buoy in nearshore conditions

Cited by 15 publications

References 20 publications

Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar

Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar

Floating Doppler Wind Lidar Measurement of Wind Turbulence: A Cluster Analysis

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

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