scholarly journals Remote Sensing of Stable Boundary Layer of Atmosphere

2020 ◽  
Vol 237 ◽  
pp. 06015
Author(s):  
V.A. Banakh ◽  
I.N. Smalikho ◽  
A.V. Falits
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Dissipation Rate ◽  
Richardson Number ◽  
Stream Line ◽  
Outer Scale ◽  
Stable Boundary ◽  
Wind Lidar ◽  
The Stability ◽  
Doppler Wind Lidar

The stability of the atmospheric boundary layer (ABL) has been studied experimentally with a Stream Line coherent Doppler wind lidar and an MTP-5 temperature profilometer. Two-dimensional height-time distributions of the parameters characterizing the ABL temperature regime and wind turbulence, namely, the Richardson number, the dissipation rate of the kinetic energy of turbulence, the variance of fluctuations of the radial velocity, and the outer scale of turbulence, have been obtained. The amplitudes and period of oscillations of wind velocity components during atmospheric waves arising in the stable ABL have been determined.

Observational Support for the Stability Dependence of the Bulk Richardson Number Across the Stable Boundary Layer

2013 ◽  
Vol 150 (3) ◽  
pp. 515-523 ◽  
Author(s):  
S. Basu ◽  
A. A. M. Holtslag ◽  
L. Caporaso ◽  
A. Riccio ◽  
G.-J. Steeneveld
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Bulk Richardson Number ◽  
Stable Boundary ◽  
The Stability

scholarly journals The Critical Richardson Number and Limits of Applicability of Local Similarity Theory in the Stable Boundary Layer

2012 ◽  
Vol 147 (1) ◽  
pp. 51-82 ◽  
Author(s):  
Andrey A. Grachev ◽  
Edgar L Andreas ◽  
Christopher W. Fairall ◽  
Peter S. Guest ◽  
P. Ola G. Persson
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Similarity Theory ◽  
Local Similarity ◽  
Stable Boundary ◽  
Critical Richardson Number ◽  
Local Similarity Theory

scholarly journals Wind–Temperature Regime and Wind Turbulence in a Stable Boundary Layer of the Atmosphere: Case Study

2020 ◽  
Vol 12 (6) ◽  
pp. 955 ◽  
Author(s):  
Viktor A. Banakh ◽  
Igor N. Smalikho ◽  
Andrey V. Falits
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Atmospheric Waves ◽  
Stable Boundary ◽  
Low Level ◽  
Wind Turbulence ◽  
Stable Atmospheric Boundary Layer ◽  
Low Level Jets

The paper presents the results of probing the stable atmospheric boundary layer in the coastal zone of Lake Baikal with a coherent Doppler wind lidar and a microwave temperature profiler. Two-dimensional height–temporal distributions of the wind velocity vector components, temperature, and parameters characterizing atmospheric stability and wind turbulence were obtained. The parameters of the low-level jets and the atmospheric waves arising in the stable boundary layer were determined. It was shown that the stable atmospheric boundary layer has an inhomogeneous fine scale layered structure characterized by strong variations of the Richardson number Ri. Layers with large Richardson numbers alternate with layers where Ri is less than the critical value of the Richardson number Ricr = 0.25. The channels of decreased stability, where the conditions are close to neutral stratification 0 < Ri < 0.25, arise in the zone of the low-level jets. The wind turbulence in the central part of the observed jets, where Ri > Ricr, is weak, increases considerably to the periphery of jets, at heights where Ri < Ricr. The turbulence may intensify at the appearance of internal atmospheric waves.

scholarly journals High-Resolution In Situ Profiling through the Stable Boundary Layer: Examination of the SBL Top in Terms of Minimum Shear, Maximum Stratification, and Turbulence Decrease

2006 ◽  
Vol 63 (4) ◽  
pp. 1291-1307 ◽  
Author(s):  
B. B. Balsley ◽  
R. G. Frehlich ◽  
M. L. Jensen ◽  
Y. Meillier
Keyword(s):  
Boundary Layer ◽  
Energy Dissipation ◽  
High Resolution ◽  
Stable Boundary Layer ◽  
Dissipation Rate ◽  
Energy Dissipation Rate ◽  
Horizontal Wind ◽  
Small Scale ◽  
Stable Boundary ◽  
Stable Conditions

Abstract Some 50 separate high-resolution profiles of small-scale turbulence defined by the energy dissipation rate (ɛ), horizontal wind speed, and temperature from near the surface, through the nighttime stable boundary layer (SBL), and well into the residual layer are used to compare the various definitions of SBL height during nighttime stable conditions. These profiles were obtained during postmidnight periods on three separate nights using the Tethered Lifting System (TLS) during the Cooperative Atmosphere–Surface Exchange Study (CASES-99) campaign in east-central Kansas, October 1999. Although the number of profiles is insufficient to make any definitive conclusions, the results suggest that, under most conditions, the boundary layer top can be reasonably estimated in terms of a very significant decrease in the energy dissipation rate (i.e., the mixing height) with height. In the majority of instances this height lies slightly below the height of a pronounced minimum in wind shear and slightly above a maximum in N 2, where N is the Brunt–Väisälä frequency. When combined with flux measurements and vertical velocity variance data obtained from the nearby 55-m tower, the results provide additional insights into SBL processes, even when the boundary layer, by any definition, extends to heights well above the top of the tower. Both the TLS profiles and tower data are then used for preliminary high-resolution studies into various categories of SBL structure, including the so-called upside-down boundary layer.

scholarly journals Lidar study of wind turbulence, low level jet streams, and atmospheric internal waves in the boundary layer of atmosphere

2018 ◽  
Vol 176 ◽  
pp. 06005
Author(s):  
Viktor Banakh ◽  
Igor Smalikho
Keyword(s):  
Boundary Layer ◽  
Internal Waves ◽  
Stable Boundary Layer ◽  
Dissipation Rate ◽  
Jet Streams ◽  
Stable Boundary ◽  
Low Level ◽  
Experimental Campaign ◽  
Wind Turbulence ◽  
Low Level Jet

The results of lidar study of wind turbulence, low level jet streams, and internal atmospheric waves in the stable boundary layer of atmosphere on the coast of Lake Baikal are presented. Few events of the atmospheric internal waves (AIWs) were registered during the experimental campaign. All the registered AIWs were observed in the presence of low level jet streams. Two dimensional time–height patterns of the wind turbulence dissipation rate during AIW events were obtained as well.

scholarly journals On the computation of planetary boundary layer height using the bulk Richardson number method

2014 ◽  
Vol 7 (3) ◽  
pp. 4045-4079 ◽  
Author(s):  
Y. Zhang ◽  
Z. Gao ◽  
D. Li ◽  
Y. Li ◽  
N. Zhang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Bulk Richardson Number ◽  
Stable Boundary ◽  
Boundary Layer Height ◽  
Unstable Boundary Layer ◽  
Richardson Method ◽  
Field Campaigns ◽  
Weakly Stable

Abstract. Experimental data from four intensive field campaigns are used to explore the variability of the critical bulk Richardson number, which is a key parameter for calculating the planetary boundary layer height (PBLH) in numerical weather and climate models with the bulk Richardson method. First, the PBLHs of three different thermally-stratified boundary layers (i.e., strongly stable boundary layer, weakly stable boundary layer, and unstable boundary layer) from the four field campaigns are determined using the turbulence method, the potential temperature gradient method, the low-level jet method, or the modified parcel method. Then for each type of boundary layer, an optimal critical Richardson numbers is obtained through linear fitting and statistical error minimization methods so that the bulk Richardson method with this optimal critical bulk Richardson number yields similar estimates of PBLHs as the methods mentioned above. We find that the optimal critical bulk Richardson number increases as the atmosphere becomes more unstable: 0.24 for strongly stable boundary layer, 0.31 for weakly stable boundary layer, and 0.39 for unstable boundary layer. Compared with previous schemes that use a single value of critical bulk Richardson number for calculating the PBLH, the new values of critical bulk Richardson number that proposed by this study yield more accurate estimate of PBLH.

Evaluating Formulations of Stable Boundary Layer Height

2004 ◽  
Vol 43 (11) ◽  
pp. 1736-1749 ◽  
Author(s):  
D. Vickers ◽  
L. Mahrt
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Critical Value ◽  
Buoyancy Flux ◽  
Turbulence Energy ◽  
Bulk Richardson Number ◽  
Layer Height ◽  
Stable Boundary ◽  
Boundary Layer Height

Abstract Stable boundary layer height h is determined from eddy correlation measurements of the vertical profiles of the buoyancy flux and turbulence energy from a tower over grassland in autumn, a tower over rangeland with variable snow cover during winter, and aircraft data in the stable marine boundary layer generated by warm air advection over a cool ocean surface in summer. A well-defined h within the tower layer at the grass site (lowest 50 m) and the snow site (lowest 30 m) was definable only about 20% of the time. In the remaining stable periods, the buoyancy flux and turbulence energy either (a) remained constant with height, indicating a deep boundary layer, (b) increased with height, or (c) varied erratically with height. Approximately one-half of the tower profiles did not fit the traditional concepts of a boundary layer. The well-defined cases of h are compared with various formulations for the equilibrium depth of the stably stratified boundary layer based on the Richardson number or surface fluxes. The diagnostic models for h have limited success in explaining both the variance and mean magnitude of h at all three sites. The surface bulk Richardson number and gradient Richardson number approaches perform best for the combined data. For the surface bulk Richardson number method, the required critical value varies systematically between sites. The surface bulk Richardson number approach is modified to include a critical value that depends on the surface Rossby number, which incorporates the influence of surface roughness and wind speed on boundary layer depth.

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