Abstract
The height of the stable boundary layer (SBL), known as the nocturnal boundary layer height, is controlled by numerous factors of different natures. The SBL height defines the state of atmospheric turbulence and describes the diffusion capacity of the atmosphere. Therefore, it is unsurprising that many alternative (sometimes contradictory) formulations for the SBL height have been proposed to date, and no consensus has been achieved. In our study, we propose an iterative algorithm to determine the SBL height h based on the flux–profile relationship using wind profiles and turbulent fluxes. This iterative algorithm can obtain temporally continuous, accurate estimates of h and is widely applicable. The predicted h presents relatively good agreement with four observation-derived SBL heights, hJ, h1, hi, and hθ (hJ: maximum wind speed height, h1: zero wind shear height, hi: temperature inversion height, and hθ: height at which 0.8 times the inversion strength appears for the first time), especially with hθ, which shows the best fit. In addition, h exhibits a low absolute difference and relative difference with hJ, which presents the second-best result. The agreement with hi and h1 may be satisfactory, but small differences are observed, and the one standard deviation of the mean relative difference is large. In addition, the predicted h is compared with other SBL height estimation methods, including diagnostic, λ1, λ2 and λ3 (three typical dimensional scale height parameters) and prognostic equation-based methods, λ(h) (an equation for the growth of h developed). The diagnostic formulas are found to be appropriate, especially under extremely stable conditions. Additionally, the equation of λ3 presents the best result of all the dimensional scale height parameters. However, the prognostic equation λ(h) in our study is very unsatisfactory.
Highlights
The atmospheric boundary layer (ABL) height must be known for a number of practical applications, for example, when modeling the dispersion of pollutants, where the upper boundary of the turbulent layer plays a role as an impenetrable barrier to pollutants released at the surface [4,5,6,7]
[42,50] proposed proposed aa diagnostic diagnostic equation ciprocals ciprocals of of hh were weresatisfied satisfiedwith withsome somesecond-order second-order linear linear interpolation interpolation terms terms using using aa large-eddy simulation (LES) database on a stable and neutral atmospheric boundary layer: large-eddy simulation (LES) database on a stable and neutral atmospheric boundary layer: Figure 3
The regression parameters of Ep. 3, including the correlation coefficient and slope of the regression lines, show the highest values in all four episodes, indicating that the hJ of Ep. 3 fits the h calculated in this study the best, mainly because h is often associated with high wind shear U(z), which is more likely to attenuate the turbulent kinetic energy (TKE) at the height of the wind profile maximum
Summary
Some researchers determined h based on vertical profiles of temperature, humidity, and wind, e.g., in [18,19,20,21,22,23,24,25,26] These approaches suffered from many shortcomings, such as crossing the SBL along a slanted path within a few minutes, providing a “snapshot”-like profile, having a limited height resolution of routine ascents, the impossibility of obtaining measurements under high wind speeds, noncontinuous observations, and a lack of turbulent fluxes [27]. 2020) in Xilin Gol League, Inner Mongolia, China, to measure the aerosol-cloud-boundary layer interaction; the vegetation in the study region is dominated by grasses, and there are no residential or industrial areas near this field (Figure 1, 42◦ 110 N, 114◦ 560 E). Can be found in the literature [44]
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