Optimization of the Cross-Correlation Algorithm for Two-Component Wind Field Estimation from Single Aerosol Lidar Data and Comparison with Doppler Lidar

Abstract

AbstractNumerical and field experiments were conducted to test an optimized cross-correlation algorithm (CCA) for the remote sensing of two-component wind vectors from horizontally scanning elastic backscatter lidar data. Each vector is the result of applying the algorithm to a square and contiguous subset of pixels (an interrogation window) in the lidar scan area. Synthetic aerosol distributions and flow fields were used to investigate the accuracy and precision of the technique. Results indicate that in neutral static stability, when the mean flow direction over the interrogation window is relatively uniform, the random error of the estimates increases as the mean wind speed and turbulence intensity increases. In convective conditions, larger errors may occur as a result of the cellular nature of convection and the dramatic changes in wind direction that may span the interrogation window. Synthetic fields were also used to determine the significance of various image processing and numerical steps used in the CCA. Results show that an iterative approach that dynamically reduces the block size provides the largest performance gains. Finally, data from a field experiment conducted in 2013 in Chico, California, are presented. Comparisons with Doppler lidar data indicate excellent agreement for the 10-min mean wind velocity computed over a set of 150 h: the root-mean-square deviations (and slopes) for the u and υ components are 0.36 m s−1 (0.974) and 0.37 m s−1 (0.991), respectively, with correlation coefficients > 0.99.