A Time Series Sodar Simulator Based on Large-Eddy Simulation

Wainwright, Charlotte E.; Stepanian, Phillip M.; Chilson, Phillip B.; Palmer, Robert D.; Fedorovich, Evgeni; Gibbs, Jeremy A.

doi:10.1175/jtech-d-13-00161.1

Cited by 15 publications

(22 citation statements)

References 29 publications

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“…All three of these techniques also assume the three-dimensional flow is horizontally homogeneous across the scanning circle used by the lidar, which is often not a valid assumption (e.g., Wainwright et al, 2014;Lundquist et al, 2015), especially in complex terrain (e.g., Bingöl et al, 2009). All lidar scanning strategies are subject to sources of error, and the magnitude of these errors is largely dependent on atmospheric stability, measurement height, and the particular type of lidar used (e.g., Sathe et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of three lidar scanning strategies for turbulence measurements

et al. 2016

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Abstract.Several errors occur when a traditional Doppler beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

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Section: Introductionmentioning

confidence: 99%

Evaluation of three lidar scanning strategies for turbulence measurements

et al. 2016

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“…A major assumption in these techniques is that the mean flow is horizontally homogeneous throughout the scanning circle, which is often invalid in complex terrain (e.g., Bingöl et al and under convective conditions , causing errors in lidar‐measured wind speeds. The DBS and VAD techniques can also cause errors in the measurement of turbulent quantities.…”

Section: Introductionmentioning

confidence: 99%

“…Both of these strategies involve directing the laser beam to different points around a scanning circle and using geometrical considerations to estimate the mean wind speed and direction. A major assumption in these techniques is that the mean flow is horizontally homogeneous throughout the scanning circle, which is often invalid in complex terrain (e.g., Bingöl et al 4 ), within wind turbine wakes 5 and under convective conditions, 6 causing errors in lidar-measured wind speeds. The DBS and VAD techniques can also cause errors in the measurement of turbulent quantities.…”

Section: Introductionmentioning

confidence: 99%

Testing and validation of multi‐lidar scanning strategies for wind energy applications

et al. 2016

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Several factors cause lidars to measure different values of turbulence than an anemometer on a tower, including volume averaging, instrument noise and the use of a scanning circle to estimate the wind field. One way to avoid the use of a scanning circle is to deploy multiple scanning lidars and point them toward the same volume in space to collect velocity measurements and extract high-resolution turbulence information. This paper explores the use of two multi-lidar scanning strategies, the tri-Doppler technique and the virtual tower technique, for measuring 3-D turbulence. In summer 2013, a vertically profiling Leosphere WindCube lidar and three Halo Photonics Streamline lidars were operated at the Southern Great Plains Atmospheric Radiation Measurement site to test these multi-lidar scanning strategies. During the first half of the field campaign, all three scanning lidars were pointed at approximately the same point in space and a tri-Doppler analysis was completed to calculate the three-dimensional wind vector every second. Next, all three scanning lidars were used to build a 'virtual tower' above the WindCube lidar. Results indicate that the tri-Doppler technique measures higher values of horizontal turbulence than the WindCube lidar under stable atmospheric conditions, reduces variance contamination under unstable conditions and can measure high-resolution profiles of mean wind speed and direction. The virtual tower technique provides adequate turbulence information under stable conditions but cannot capture the full temporal variability of turbulence experienced under unstable conditions because of the time needed to readjust the scans.

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“…For example, the DBS technique used by the WC requires the assumption that the instantaneous flow field is uniform across the scanning circle. However, this assumption is generally not true in turbulent flow, when the wind field changes significantly in both space and time (e.g., Wainwright et al, 2014;Lundquist et al, 2015). As the WC scanning circle has a diameter of 106 m at a measurement height of 100 m above ground level (AGL), it is likely that the instantaneous flow field changes in space, even in flat terrain.…”

Section: Errors In Lidar Datamentioning

confidence: 99%

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Newman

Clifton

2017

Wind Energ. Sci.

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Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices.L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physicsbased corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

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A Time Series Sodar Simulator Based on Large-Eddy Simulation

Cited by 15 publications

References 29 publications

Evaluation of three lidar scanning strategies for turbulence measurements

Evaluation of three lidar scanning strategies for turbulence measurements

Testing and validation of multi‐lidar scanning strategies for wind energy applications

An error reduction algorithm to improve lidar turbulence estimates for wind energy

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