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Measurements of turbulence are essential to understand and quantify the transport and dispersal of heat, moisture, momentum, and trace gases within the planetary boundary layer (PBL). Through the years, various techniques to measure turbulence using Doppler lidar observations have been proposed. However, the accuracy of these measurements has rarely been validated against trusted in situ instrumentation. Herein, data from the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) are used to verify Doppler lidar turbulence profiles through comparison with sonic anemometer measurements. For 17 days at the end of the experiment, a single scanning Doppler lidar continuously cycled through different turbulence measurement strategies: velocity-azimuth display (VAD), six-beam scans, and range-height indicators (RHIs) with a vertical stare. Measurements of turbulence kinetic energy (TKE), turbulence intensity, and stress velocity from these techniques are compared with sonic anemometer measurements at six heights on a 300m tower. The six-beam technique is found to generally measure turbulence kinetic energy and turbulence intensity the most accurately at all heights (r² ≈0.78), showing little bias in its observations (slope of ≈ 0:95). Turbulence measurements from the velocity-azimuth display method tended to be biased low near the surface, as large eddies were not captured by the scan. None of the methods evaluated were able to consistently accurately measure the shear velocity (r² =0.15-0.17). Each of the scanning strategies assessed had its own strengths and limitations that need to be considered when selecting the method used in future experiments. [ABSTRACT FROM AUTHOR]

The depth h of the stable boundary layer (SBL) has long been an elusive measurement. In this diagnostic study the use of high-quality, high-resolution (Δz = 10 m) vertical profile data of the mean wind U(z) and streamwise variance σu2(z) is investigated to see whether mean-profile features alone can be equated with h. Three mean-profile diagnostics are identified: hJ, the height of maximum low-level-jet (LLJ) wind speed U in the SBL; h1, the height of the first zero crossing or minimum absolute value of the magnitude of the shear ∂U/∂z profile above the surface; and h2, the minimum in the curvature ∂2U/∂z2 profile. Boundary layer BL here is defined as the surface-based layer of significant turbulence, so the top of the BL was determined as the first significant minimum in the σu2(z) profile, designated as hσ. The height hσ was taken as a reference against which the three mean-profile diagnostics were tested. Mean-wind profiles smooth enough to calculate second derivatives were obtained by averaging high-resolution Doppler lidar profile data, taken during two nighttime field programs in the Great Plains, over 10-min intervals. Nights are chosen for study when the maximum wind speed in the lowest 200 m exceeded 5 m s-1 (i.e., weak-wind, very stable BLs were excluded). To evaluate the three diagnostics, data from the 14-night sample were divided into three profile shapes: Type I, a traditional LLJ structure with a distinct maximum or “nose,” Type II, a “flat” structure with constant wind speed over a significant depth, and Type III, having a layered structure to the shear and turbulence in the lower levels. For Type I profiles, the height of the jet nose hJ, which coincided with h1 and h2 in this case, agreed with the reference SBL depth to within 5%. The study had two major results: 1) among the mean-profile diagnostics for h, the curvature depth h2 gave the best results; for the entire sample, h2 agreed with hσ to within 12%; 2) considering the profile shapes, the layered Type III profiles gave the most problems. When these profiles could be identified and eliminated from the sample, regression and error statistics improved significantly: mean relative errors of 8% for hJ and h1, and errors of <5% for h2, were found for the sample of only Type I and II profiles. [ABSTRACT FROM AUTHOR]

Quantitative data on turbulence variables aloft—above the region of the atmosphere conveniently measured from towers—have been an important but difficult measurement need for advancing understanding and modeling of the stable boundary layer (SBL). Vertical profiles of streamwise velocity variances obtained from NOAA’s high-resolution Doppler lidar (HRDL), which have been shown to be approximately equal to turbulence kinetic energy (TKE) for stable conditions, are a measure of the turbulence in the SBL. In the present study, the mean horizontal wind component U and variance σ2u were computed from HRDL measurements of the line-of-sight (LOS) velocity using a method described by Banta et al., which uses an elevation (vertical slice) scanning technique. The method was tested on datasets obtained during the Lamar Low-Level Jet Project (LLLJP) carried out in early September 2003, near the town of Lamar in southeastern Colorado. This paper compares U with mean wind speed obtained from sodar and sonic anemometer measurements. The results for the mean U and mean wind speed measured by sodar and in situ instruments for all nights of LLLJP show high correlation (0.71–0.97), independent of sampling strategies and averaging procedures, and correlation coefficients consistently >0.9 for four high-wind nights, when the low-level jet speeds exceeded 15 m s-1 at some time during the night. Comparison of estimates of variance, on the other hand, proved sensitive to both the spatial and temporal averaging parameters. Several series of averaging tests are described, to find the best correlation between TKE calculated from sonic anemometer data at several tower levels and lidar measurements of horizontal-velocity variance σ2u. Because of the nonstationarity of the SBL data, the best results were obtained when the velocity data were first averaged over intervals of 1 min, and then further averaged over 3–15 consecutive 1-min intervals, with best results for the 10- and 15-min averaging periods. For these cases, correlation coefficients exceeded 0.9. As a part of the analysis, Eulerian integral time scales (τ) were estimated for the four high-wind nights. Time series of τ through each night indicated erratic behavior consistent with the nonstationarity. Histograms of τ showed a mode at 4–5 s, but frequent occurrences of larger τ values, mostly between 10 and 100 s. [ABSTRACT FROM AUTHOR]

Profiles of mean winds and turbulence were measured by the High Resolution Doppler lidar in the strong-wind stable boundary layer (SBL) with continuous turbulence. The turbulence quantity measured was the variance of the streamwise wind velocity component σ2u. This variance is a component of the turbulence kinetic energy (TKE), and it is shown to be numerically approximately equal to TKE for stable conditions—profiles of σ2u are therefore equivalent to profiles of TKE. Mean-wind profiles showed low-level jet (LLJ) structure for most of the profiles, which represented 10-min averages of mean and fluctuating quantities throughout each of the six nights studied. Heights were normalized by the height of the first LLJ maximum above the surface ZX, and the velocity scale used was the speed of the jet UX, which is shown to be superior to the friction velocity u* as a velocity scale. The major results were 1) the ratio of the maximum value of the streamwise standard deviation to the LLJ speed σu/UX was found to be 0.05, and 2) the three most common σ2u profile shapes were determined by stability (or Richardson number Ri). The least stable profile shapes had the maximum σ2u at the surface decreasing to a minimum at the height of the LLJ; profiles that were somewhat more stable had constant σ2u through a portion of the subjet layer; and the most stable of the profiles had a maximum of σ2u aloft, although it is important to note that the Ri for even the most stable of the three profile categories averaged less than 0.20. The datasets used in this study were two nights from the Cooperative Atmosphere–Surface Exchange Study 1999 campaign (CASES-99) and four nights from the Lamar Low-Level Jet Project, a wind-energy experiment in southeast Colorado, during September 2003. [ABSTRACT FROM AUTHOR]