Stable Boundary Layer Depth from High-Resolution Measurements of the Mean Wind Profile

摘要

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 (Az = 10 m) vertical profile data of the mean wind U(z) and streamwise variance omega_u~2(z) is investigated to see whether mean-profile features alone can be equated with h. Three mean-profile diagnostics are identified: _1, the height of maximum low-level-jet (LLJ) wind speed U in the SBL; hi, the height of the first zero crossing or minimum absolutevalue of the magnitude of the shear dU/dz profile above the surface; and h_2, the minimum in the curvature delta~2U/delta z~2 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 omega_u~2(z) profile, designated as h_(omega). The height h_(omega) was taken as a reference against which the three mean-profile diagnostics were tested. Mean-wind profiles smooth enough to calculate secondderivatives 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 exceeded5 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 h_1 and h_2 in this case, agreed with the reference SBL depth to within 5 %. The study had two maj or results: 1) among the mean-profile diagnostics for h, the curvature depth h_2 gave the best results; for the entire sample, h_2 agreed with h_(omega) 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 hi, and errorsof <5% for h_2, were found for the sample of only Type I and II profiles.