A pulsed coherent CO<sub>2</sub> lidar for boundary‐layer meteorology

Pearson, Guy N.; Collier, C. G.

doi:10.1002/qj.49712555918

Cited by 25 publications

(17 citation statements)

References 38 publications

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“…Alternatively, the signal-to-noise ratio (SNR) can also be used to approximate the measurement error (Pearson and Collier, 1999). Correction for the attenuation error requires knowledge of three dimensional spatial statistics (Mann et al, 2010;Frehlich, 1997), and the accuracy of this correction depends on the suitability of the selected turbulence spectrum model and its parametrization (Sathe and Mann, 2013;Mann et al, 2010).…”

Section: Motivation and Approachmentioning

confidence: 99%

“…The bias introduced by the measurement error is negligible for a lidar measurement with a high pulse accumulation number (e.g., 20 000 for the Galion lidar) (Frehlich, 2001(Frehlich, , 1997. The random measurement error inv R is modeled by an independent Gaussian random variable with zero mean and variance σ 2 R , which is inversely proportional to the SNR (Pearson and Collier, 1999;Frehlich and Yadlowsky, 1994). Turbulence can cause σ 2 R to increase by an amount that is proportional to the range gate size and the turbulent kinetic energy (TKE) dissipation rate (Frehlich, 1997).…”

Section: Errors In Radial Velocity Variancementioning

confidence: 99%

“…Therefore, it is a good approximation to estimate σ 2 R from the SNR data using Eq. (5) in Pearson and Collier (1999) for the Galion lidar .…”

Section: Errors In Radial Velocity Variancementioning

confidence: 99%

“…Measurement errors were corrected in lidar radial velocity variance estimates using the SNR data according to Eq. (5) in Pearson and Collier (1999). Radial velocity variance from the sonic data is estimated by extrapolating the autocovariance of the first 10 time lags to lag zero, assuming that autocovariance is linearly proportional to τ 2/3 for the first 10 lags (Lenschow et al, 2000).…”

Section: Experiments Setupmentioning

confidence: 99%

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Errors in radial velocity variance from Doppler wind lidar

et al. 2016

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Abstract. A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Using both statistically simulated and observed data, this paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, the systematic error is negligible but the random error exceeds about 10 %.

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Section: Motivation and Approachmentioning

confidence: 99%

Section: Errors In Radial Velocity Variancementioning

confidence: 99%

“…Therefore, it is a good approximation to estimate σ 2 R from the SNR data using Eq. (5) in Pearson and Collier (1999) for the Galion lidar .…”

Section: Errors In Radial Velocity Variancementioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

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Errors in radial velocity variance from Doppler wind lidar

et al. 2016

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“…The precision of v R is bounded by the Cramer-Rao Bound (CRB) which is a function of SNR (Pearson and Collier, 1999). The relationship between CRB and SNR derived from Eq.…”

Section: Uncertainty In the Lidar Radial Velocitymentioning

confidence: 99%

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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Lidar observations of flow variability over complex terrain

Barkwith

Collier

2011

Meteorological Applications

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This paper examines one way in which remote sensing instrumentation can be used to advance our understanding of the interactions between complex terrain and the atmospheric boundary layer (ABL). When mean flow speed is of moderate strength and the ABL is stable, mechanical effects will dominate thermal effects in modifying flow speed and direction. Boundary layer measurements were made using the scanning Salford 10 µm pulsed CO 2 Doppler lidar during the 2005 Convective Storm Initiation Project (CSIP), above the heterogeneous orography that surrounds Faccombe, Hampshire, UK. A new method of detecting boundary layer flow perturbations was developed and successfully applied to the lidar data, giving a clearer insight into flow modification that occurs above complex terrain.

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A pulsed coherent CO₂ lidar for boundary‐layer meteorology

Cited by 25 publications

References 38 publications

Errors in radial velocity variance from Doppler wind lidar

Errors in radial velocity variance from Doppler wind lidar

Lidar arc scan uncertainty reduction through scanning geometry optimization

Lidar observations of flow variability over complex terrain

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A pulsed coherent CO2 lidar for boundary‐layer meteorology

Cited by 25 publications

References 38 publications

Errors in radial velocity variance from Doppler wind lidar

Errors in radial velocity variance from Doppler wind lidar

Lidar arc scan uncertainty reduction through scanning geometry optimization

Lidar observations of flow variability over complex terrain

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A pulsed coherent CO₂ lidar for boundary‐layer meteorology