Performance evaluation of a floating Doppler wind lidar buoy in mediterranean near-shore conditions

Gutiérrez, Miguel Ángel Caballero; Tiana-Alsina, Jordi; Bischoff, Oliver; Cateura, Jordi; Rocadenbosch, Francesc

doi:10.1109/igarss.2015.7326228

Cited by 6 publications

(9 citation statements)

References 1 publication

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“…Specifically, the default 30 % 1 s KPI compliance when no filtering procedure is applied [case (i)] is raised to some 80 % compliance when window averaging is applied [case (ii)]. Likewise, when considering HWS results for the whole campaign (Section 4.4), the width of the 1 s HWS error histogram is reduced from RMSE

_{i} = 0.51 m s^{- 1}

(12.24 % , Table ) to RMSE

_{ii} = 0.34 m s^{- 1}

(7.39 % , Table ). When considering TI (10 min data, Section 4.5), the offset between the ‘reference’ and the ‘floating’ lidar is reduced from n i =−0.0157 to n ii =−610 −4 .…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation of a floating lidar buoy in nearshore conditions

2017

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This work provides a signal-processing and statistical-error analysis methodology to assess key performance indicators for a floating Doppler wind lidar. The study introduces the raw-to-clean data processing chain, error assessment indicators and key performance indicators, as well as two filtering methods at post-processing level to alleviate the impact of angular motion and spatial variability of the wind flow on the performance indicators. Towards this aim, the study mainly revisits horizontal wind speed (HWS) and turbulence intensity measurements with a floating ZephIR 300 lidar buoy during a 38 day nearshore test campaign in Pont del Petroli (Barcelona). Typical day cases along with overall statistics for the whole campaign are discussed to illustrate the methodology and processing tools developed.Peer ReviewedPostprint (author's final draft

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_{i} = 0.51 m s^{- 1}

(12.24 % , Table ) to RMSE

_{ii} = 0.34 m s^{- 1}

(7.39 % , Table ). When considering TI (10 min data, Section 4.5), the offset between the ‘reference’ and the ‘floating’ lidar is reduced from n i =−0.0157 to n ii =−610 −4 .…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation of a floating lidar buoy in nearshore conditions

2017

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“…The MD accounts for the systematic error in the LiDAR-measured TI (equivalently, HWS standard deviation) caused by wave-induced motion [9,46]. The RMSE and MD definitional formulae to compare FDWL uncorrected measurements to fixed-LiDAR measurements are analogous to Equations ( 40) and ( 41) above, by changing TI f loat.,corr to TI f loat.…”

Section: Ukf Resultsmentioning

confidence: 99%

“…Multiple validation campaigns have shown the robustness and reliability of horizontal wind speed (HWS) and wind direction (WD) FDWL measurements at the ten-minute level [9][10][11][12][13]. However, FDWLs measure an increased turbulence intensity (TI), in contrast to fixed Doppler Wind LiDARs (DWLs), due to wave-induced motion [14].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity–azimuth display algorithm, and a wind model describing the LiDAR-retrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at “El Pont del Petroli”, Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from −1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93.

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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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Performance evaluation of a floating Doppler wind lidar buoy in mediterranean near-shore conditions

Cited by 6 publications

References 1 publication

Performance evaluation of a floating lidar buoy in nearshore conditions

Performance evaluation of a floating lidar buoy in nearshore conditions

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

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

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