Articles | Volume 9, issue 12
Research article
06 Dec 2016
Research article |  | 06 Dec 2016

Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations

Timothy A. Bonin, Jennifer F. Newman, Petra M. Klein, Phillip B. Chilson, and Sonia Wharton

Abstract. Since turbulence measurements from Doppler lidars are being increasingly used within wind energy and boundary-layer meteorology, it is important to assess and improve the accuracy of these observations. While turbulent quantities are measured by Doppler lidars in several different ways, the simplest and most frequently used statistic is vertical velocity variance (w2) from zenith stares. However, the competing effects of signal noise and resolution volume limitations, which respectively increase and decrease w2, reduce the accuracy of these measurements. Herein, an established method that utilises the autocovariance of the signal to remove noise is evaluated and its skill in correcting for volume-averaging effects in the calculation of w2 is also assessed. Additionally, this autocovariance technique is further refined by defining the amount of lag time to use for the most accurate estimates of w2. Through comparison of observations from two Doppler lidars and sonic anemometers on a 300 m tower, the autocovariance technique is shown to generally improve estimates of w2. After the autocovariance technique is applied, values of w2 from the Doppler lidars are generally in close agreement (R2 ≈ 0.95 − 0.98) with those calculated from sonic anemometer measurements.

Short summary
Turbulence measurements are important to boundary layer meteorology and related fields. Doppler lidars are capable of providing continuous profiles of turbulence statistics. Herein, the most direct turbulence measurement, vertical velocity variance, is validated with those from sonic anemometers. Spectra are also compared. A method of calculating velocity variance using the autocovariance is shown to improve the accuracy of the measurement by mitigating effects of noise and averaging.