Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer

Banakh, V. A.; Smalikho, Igor N.; Köpp, F.; Werner, Christian

doi:10.1364/ao.34.002055

Cited by 54 publications

(45 citation statements)

References 13 publications

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“…Note that Eq. (11) misrepresents properties of turbulence of small scales and thus is not consistent with the von Kármán model used to calculate L u in Appendix A, but it has similar accuracy to the von Kármán model in predicting the uncertainty of wind speed from conical scans (Banakh et al, 1995). Thus, Eq.…”

Section: Uncertainty Computed From An Isotropic Turbulence Modelmentioning

confidence: 97%

“…The empirical relationship between the SNR and the CRB is denoted by the dark solid line and is based on Eq. (5) in Pearson and Collier (1999). cle locations in the sensing volume can broaden the signal spectrum and thus increase the uncertainty in v R (Banakh et al, 1995;Frehlich, 1997). When the turbulence is sufficiently strong (σ v r ≥ 0.5w R where w R is the spectrum width of lidar signal in velocity space that is ∼ 0.877 m s −1 for the Galion lidar), the random error variance σ 2 e becomes proportional to σ v r .…”

Section: Uncertainty In the Lidar Radial Velocitymentioning

confidence: 99%

“…In this section, we describe how the uncertainty in the estimated mean horizontal wind speed from arc scans (denoted as V l ) is related to the characteristics of the wind field and the scanning geometry. The method used follows that of Banakh et al (1995) which was developed to evaluate the uncertainty of wind velocities estimated from VAD scans. In a homogeneous and stationary wind field, the covariance between the ith and j th radial velocities, which are measured by a lidar at the range gates centered at r i = r i d and r j = r j d, respectively, is a function of the relative location between these two measurements.…”

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

“…where c 1 = 0.7 (Banakh et al, 1995;Frehlich and Cornman, 2002). The turbulence kinetic energy dissipation rate (ε T ) is related to the dimensionless dissipation rate (φ ε ) via the following equation:…”

Section: Appendix A: Atmospheric Boundary Layer Turbulence Characterimentioning

confidence: 99%

“…The radial velocity v r is the projection of the wind velocity u on the line of sight (LOS) at the location s = sd for which s is the distance from the lidar along the LOS and d is the unit directional vector determined by the elevation angle φ and the azimuth angle θ of the LOS from north. The uncertainty or standard error of the estimated wind velocity has great importance to wind energy applications and is a function of the atmospheric turbulence structure and the specific lidar scanning geometry (Banakh et al, 1995). Two common scanning geometries applied in wind energy are the velocity-azimuth-display (VAD) scan and the arc scan.…”

Section: Introductionmentioning

confidence: 99%

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Lidar arc scan uncertainty reduction through scanning geometry optimization

Wang¹,

Barthelmie²,

Pryor³

et al. 2016

Atmos. Meas. Tech.

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Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

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Section: Uncertainty Computed From An Isotropic Turbulence Modelmentioning

confidence: 97%

Section: Uncertainty In the Lidar Radial Velocitymentioning

confidence: 99%

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

Section: Appendix A: Atmospheric Boundary Layer Turbulence Characterimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Lidar arc scan uncertainty reduction through scanning geometry optimization

Wang¹,

Barthelmie²,

Pryor³

et al. 2016

Atmos. Meas. Tech.

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Wind turbulence estimates in a valley by coherent Doppler lidar

Krishnamurthy¹,

Calhoun²,

Billings³

et al. 2011

Meteorological Applications

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In this paper, the effect of several turbulence parameters during various flow conditions in Owens Valley, educed from coherent Doppler lidar data have been studied. Radial velocity structure functions are processed to estimate the turbulent kinetic energy (TKE) dissipation rate, integral length scale and velocity variance, assuming a theoretical model for isotropic wind fields. Corrections for turbulence measurements have been considered to address the complications due to inherent volumetric averaging of radial velocity over each range gate, noise of the lidar data, and the assumptions required to estimate effects of smaller scales of motion on turbulence quantities. Using data from the Terrain-induced Rotor Experiment (T-REX) in April-May 2006, vertical profiles of wind and turbulence parameters have been retrieved. During T-REX, unusual valley flows were detected by the lidar data, for example on 19 and 27 March 2006, daytime downvalley and night time up-valley flows, respectively, were observed. This paper focuses on understanding various turbulence parameters during these flow events. Turbulence estimates during daytime down-valley conditions were observed to be constant for most of the day, while for night time up-valley circumstances the turbulence increased steadily as the day progressed. Good comparison was observed between lidar and tower measurements, which validate the lidar turbulence retrieval assumptions. Comparison between TKE estimates from lidar and the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model is also presented. This analysis will be helpful for improving the current turbulence parameterization schemes in COAMPS. Finally, differences and similarities in turbulence measurements between both the flow regimes are discussed.

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Wind lidar evaluation at the Danish wind test site in Høvsøre

et al. 2006

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Initial assessments of a wind lidar have shown the technology to have significant potential for wind field measurements in the wind energy industry. A more extended evaluation is now reported using a scanning lidar next to a meteorological mast with calibrated anemometers at the Risø wind test site in Høvsøre on the windy northwest coast of Denmark. Results are presented of wind speed comparisons at heights up to 100 m above ground level showing excellent correlation between the lidar and the cup anemometers. Copyright © 2006 John Wiley & Sons, Ltd.

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Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer

Cited by 54 publications

References 13 publications

Lidar arc scan uncertainty reduction through scanning geometry optimization

Lidar arc scan uncertainty reduction through scanning geometry optimization

Wind turbulence estimates in a valley by coherent Doppler lidar

Wind lidar evaluation at the Danish wind test site in Høvsøre

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