Estimations of atmospheric boundary layer fluxes and other turbulence parameters from Doppler lidar data
Authors: Gal‐Chen Tzvi , Xu Mei , Eberhard Wynn L. 
Published: February 2012

Techniques for extraction of boundary layer parameters from measurements of a short pulse (≈0.4 μs) CO2 Doppler lidar (λ = 10.6 μm) are described. The lidar is operated by the National Oceanic and Atmospheric Administration (NOAA) Wave Propagation Laboratory (WPL). The measurements are those collected during the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The recorded radial velocity measurements have a range resolution of 150 m. With a pulse repetition rate of 20 Hz it is possible to perform scannings in two perpendicular vertical planes (x–z and y–z) in approximately 72 s. By continuously operating the lidar for about an hour, one can extract stable statistics of the radial velocities. Assuming that the turbulence is horizontally homogeneous, we have estimated the mean wind, its standard deviations, and the momentum fluxes. We have estimated the first, second, and, third moments of the vertical velocity from the vertically pointing beam. Spectral analysis of the radial velocities is also performed, from which (by examining the amplitude of the power spectrum at the inertial range) we have deduced the kinetic energy dissipation. Finally, using the statistical form of the Navier‐Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation. With the exception of the vertically pointing beam an individual radial velocity estimate is accurate only to ±0.7 m s−1. Combining many measurements would normally reduce the error, provided that it is unbiased and uncorrelated. The nature of some of the algorithms, however, is such that biased and correlated errors may be generated even though the “raw” measurements are not. We have developed data processing procedures that eliminate bias and minimize error correlation. Once bias and error correlations are accounted for, the large sample size is shown to reduce the errors substantially. We show, for instance, that a single momentum flux estimate has an accuracy of ±2m2s−2, but when combined with other measurements, the error can be reduced to ±0.10 m2 s−2. The principal features of the derived turbulence statistics for two case studies (July 11, 1987, 1611–1710 UTC and 1729–1810 UTC) are as follows: The mean surface wind is from the south and has a speed of approximately 15 m s−1. There is a southerly jet 1 km above the surface. The wind in the direction normal to the surface wind becomes more westerly with height. The derived momentum fluxes in the mixed layer agree with the unfiltered airplane measurements. They are of the order of approximately 0.5 m2 s−2 near the surface and are retarding the southerly wind. At the stable layer, the momentum fluxes are countergradient; they remain negative even though the southerlies are diminishing with height and are concentrated in thin layers. By spectrally analyzing the vertical beam, we have identified the Brunt‐Väisälä frequency as the dominated frequency in the stable layer. The available evidence suggests that the large countergradient fluxes are caused by a critical layer singularity. Under these conditions, gravity wave flux divergence may be large. Current general circulation models (GCMs) generally do not parameterize countergradient momentum transport. If the phenomenon is prevalent, our study suggests that inclusion of such an effect may be important. The wind spectrum near the surface has a −5/3 power law at the inertial range. The heat flux estimate is 100 ± 20 W m−2, in apparent agreement with an average estimate of the surface stations but systematically larger than the filtered airplane data.