A six-beam method to measure turbulence statistics using ground-based wind lidars

Abstract

Abstract.A so-called six-beam method is proposed to measure atmospheric turbulence using a ground-based wind lidar. This method requires measurement of the radial velocity variances at five equally spaced azimuth angles on the base of a scanning cone and one measurement at the centre of the scanning circle, i.e.using a vertical beam at the same height. The scanning configuration is optimized to minimize the sum of the random errors in the measurement of the second-order moments of the components (u, v, w) of the wind field. We present this method as an alternative to the so-called velocity azimuth display (VAD) method that is routinely used in commercial wind lidars, and which usually results in significant averaging effects of measured turbulence. In the VAD method, the high frequency radial velocity measurements are used instead of their variances. The measurements are performed using a pulsed lidar (WindScanner), and the derived turbulence statistics (using both methods) such as the u and v variances are compared with those obtained from a reference cup anemometer and a wind vane at 89 m height under different atmospheric stabilities. The measurements show that in comparison to the reference cup anemometer, depending on the atmospheric stability and the wind field component, the six-beam method measures between 85 and 101 % of the reference turbulence, whereas the VAD method measures between 66 and 87 % of the reference turbulence.

Ten Years of Boundary-Layer and Wind-Power Meteorology at Høvsøre, Denmark

“…The cross-contamination effect is minimized using this method, but compensating for the spatial averaging effects for pulsed lidars still remains a challenge. Experimental evidence suggests that the six-beam method partly overcomes the problem of probe volume averaging that is inherent in the VAD method (Sathe et al 2015).…”

Errors in radial velocity variance from Doppler wind lidar

“…Accordingly, uncertainties in lidar-derived mean wind velocity estimates have been well characterized Lindelöw-Marsden, 2009) and methods and procedures have been developed for error reduction and uncertainty control (Clifton et al, 2013;Gottschall et al, 2012). However, use of lidar for turbulence measurements, while possible (Newman et al, 2016;Branlard et al, 2013;Mann et al, 2010), is less established (Sathe et al, 2015;Sathe and Mann, 2013). Two methods are commonly used to derive the second-order moments (i.e., velocity variances and momentum fluxes) of turbulent flow from lidar data (Sathe and Mann, 2013).…”

“…Mann et al (2010) call for a thorough sampling error analysis in order to understand the difference between radial velocity variance and momentum fluxes derived from lidars and sonic anemometers. The size of sampling error is a function of the sampling interval and duration (Lenschow et al, 1994), both of which are determined by lidar scan geometries which can, in turn, be optimized to minimize the uncertainty in the estimated turbulence statistics (Sathe et al, 2015). Improved understanding of sampling errors in lidar-derived radial velocity variance estimates is a necessary prerequisite to the development of robust techniques that will enable the widespread use of lidar for high-fidelity turbulence measurements.…”

“…The first method can suffer from cross-contamination errors (biases) due to the correlation between the radial velocities used for wind velocity estimates (Sathe et al, 2011). Therefore, the second method is considered to be more suitable for lidar turbulence measurements (Newman et al, 2016;Sathe et al, 2015;Sathe and Mann, 2013;Mann et al, 2010) Using the second method mentioned above, errors in the estimated variances and momentum fluxes are accumulations of the following three types of errors in the estimated radial velocity variance:…”

“…2). For example, when a pulsed lidar is configured to estimate turbulence statistics with the six-beam method of Sathe et al (2015), the lidar samples six locations sequentially, and therefore the sampling interval (δt) at one location is 6 times the sampling interval per one lidar measurement (i.e., δt > 6 s and is at least 60 times slower than a sonic anemometer sampling at 10 Hz). Hence, the radial velocity variance from a time series of v R acquired over a period T with a sampling interval δt is estimated from…”