92 A New Technique for Reducing Variance Contamination in Lidar Turbulence Measurements

Wednesday, 22 June 2016
Alta-Deer Valley (Sheraton Salt Lake City Hotel)
Jennifer F. Newman, University of Oklahoma, Norman, OK; and P. Klein, S. Wharton, A. Sathe, and T. A. Bonin

While lidars can measure mean wind speeds with a high degree of accuracy, lidar-measured turbulence is affected by several factors, including volume averaging and instrument noise. In addition, the scanning strategy used by the lidar can induce errors in turbulence estimates. The commonly used Doppler-beam swinging (DBS) and velocity-azimuth display (VAD) strategies result in variance contamination, where additional variance components contaminate the true value of the variance. To mitigate the effects of variance contamination, a scanning strategy was recently developed which employs six beam positions to independently estimate the 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 recently deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.

Results indicate that the six-beam strategy mitigated some of the errors caused by VAD and DBS scans, but the six-beam equations can result in unrealistically low or even negative values of the horizontal velocity variances if noisy signals are measured at the different beam positions. These errors were of particular concern under low wind speed conditions. A new correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the horizontal 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 improving the accuracy of velocity variance observations with commercially available lidars. During this presentation, initial results from the new correction method will be shown and methods for further refining the method will be discussed.

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