Two-Component Horizontal Aerosol Motion Vectors in the Atmospheric Surface Layer from a Cross-Correlation Algorithm Applied to Scanning Elastic Backscatter Lidar Data*

Abstract

Two-component horizontal motion vectors of aerosol features were calculated by applying a crosscorrelation algorithm to square image blocks extracted from consecutive pairs of elastic backscatter lidar scans. The resulting vector components were compared with corresponding horizontal wind components from tower-mounted sonic anemometers located at the center of the image blocks. In the analysis 180 245 pairs of vectors derived from 75 days of field data collected between 19 March and 11 June 2007 were used. Examples of time series comparisons from 4-h periods during light, strong, and changing wind conditions are presented. Mean signal-to-noise ratios (SNRs) of the block backscatter data, maxima of the cross-correlation functions (CCFs), observed wind speed, and turbulent kinetic energy (TKE) were also calculated for each velocity component comparison. The correlation between the lidar-derived motion components and sonic anemometer wind components tends to be highest during light wind conditions with low TKE. An empirical relationship is presented that enables the elimination of vectors that are likely to be significantly different than the anemometer measurement. When applied to the entire set of scans available, this quality control (QC) method increases the correlation between the two forms of measurements. Finally, the cross-correlation algorithm and QC method are applied to a mesh of locations over pairs of scans. Two examples of twodimensional and two-component vector flow fields are shown. In one case, the flow field reveals a rotational circulation associated with a vortex and, in the other case, convergence and transport near the leading edge of a density current front.

Wind Gust Detection and Impact Prediction for Wind Turbines

“…Normalized distribution probability of the spanwise deviation of patches from the predicted points. The dashed line represents the normalized probability modeled by Equation (10), and the histogram shows the normalized probability of the real spanwise deviations.…”

“…The usage of the square root is according to the relationship between the deviation and time presented in Equation (10). The patch scale (L pat ) was normalized by the rotor diameter, i.e., l pat = L pat /D, and l pat is the dimensionless scale.…”

“…Mayor adapted two computer-vision methods for flow motion estimation: the cross-correlation method and the wavelet-based optical flow method [10,11]. However, the cross-correlation method has limitations for non-uniform velocity fields, and the optical flow method requires relatively small (few pixels) movement and is computationally demanding.…”

A review of turbulence measurements using ground-based wind lidars

“…The simple design of these instruments may eventually lead to cheaper lidar systems. Non-coherent detection may also provide possible new ways to estimate atmospheric turbulence (Mayor et al, 2012;Sela and Tsadka, 2011), but to our knowledge it does not, so far, challenge the capabilities of the coherent Doppler lidars.…”

Four Methods for LIDAR Retrieval of Microscale Wind Fields

“…An important early example is by Eloranta [6]. In contrast, a paper [7] includes a review of the technological advances in the intervening 40 years. The reference [1] contains a comparison of sodar, LIDAR, and anemometer wind measurement technologies.…”