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.

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

2014 ◽  
Vol 7 (6) ◽  
pp. 2599-2611 ◽  
Author(s):  
Y. Zhang ◽  
Z. Gao ◽  
D. Li ◽  
Y. Li ◽  
N. Zhang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Boundary Layers ◽  
Planetary Boundary Layer ◽  
Richardson Number ◽  
Potential Temperature ◽  
Bulk Richardson Number ◽  
Stable Boundary Layers ◽  
Stable Boundary ◽  
Field Campaigns ◽  
Weakly Stable

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

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.

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

On stable boundary-layer height estimation using backscatter lidar data and variance processing

2021 ◽  
Author(s):  
Marcos Paulo Araujo da Silva ◽  
Constantino Muñoz-Porcar ◽  
Umar Saeed ◽  
Francesc Rey ◽  
Maria Teresa Pay ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Stable Boundary Layer ◽  
Adaptive Estimation ◽  
Microwave Radiometer ◽  
Minimum Variance ◽  
Initial Guess ◽  
Layer Height ◽  
Stable Boundary ◽  
Boundary Layer Height ◽  
Temporal Variance

<p>This study describes a method to estimate the nocturnal stable boundary layer height (SBLH) by means of lidar observations. The method permits two approaches which yield independent retrievals through either spatial or temporal variance vertical profiles of the attenuated backscatter. Then, the minimum variance region (MVR) on this profile is identified. Eventually, when multiple MVRs are detected, a temperature-based SBLH estimation derived from radiosonde, launched within the searching time, is used to disambiguate the initial guess. In order to test the method, two study cases employing lidar-ceilometer (Jenoptik CHM 15k Nimbus) measurements are investigated. Temperature-based estimates from a collocated microwave radiometer permitted validation, using either temporal or spatial backscatter variances. The dataset was collected during the HD(CP)2 Observational Prototype Experiment (HOPE) [1].   </p><p>[1] U. Saeed, F. Rocadenbosch, and S. Crewell, “Adaptive Estimation of the Stable Boundary Layer Height Using Combined Lidar and Microwave Radiometer Observations,” IEEE Trans. Geosci. Remote Sens., 54(12), 6895–6906 (2016), DOI: 10.1109/TGRS.2016.2586298.</p><p>[2] U. Löhnert, J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O’Connor, C. Simmer, A. Wahner, and S. Crewell, “JOYCE: Jülich Observatory for Cloud Evolution,” Bulletin of the American Meteorological Society, 96(7), 1157-1174 (2015). DOI: 10.1175/BAMS-D-14-00105.1</p>

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 Shear Capacity as Prognostic for Nocturnal Boundary Layer Regimes

2015 ◽  
Vol 72 (4) ◽  
pp. 1518-1532 ◽  
Author(s):  
Ivo G. S. van Hooijdonk ◽  
Judith M. M. Donda ◽  
Herman J. H. Clercx ◽  
Fred C. Bosveld ◽  
Bas J. H. van de Wiel
Keyword(s):  
Boundary Layer ◽  
Steady State ◽  
Stable Boundary Layer ◽  
Transition Period ◽  
Nocturnal Boundary Layer ◽  
Shear Capacity ◽  
Ensemble Averaging ◽  
Similarity Parameter ◽  
Stable Boundary ◽  
Weakly Stable

Abstract Field observations and theoretical analysis are used to investigate the appearance of different nocturnal boundary layer regimes. Recent theoretical findings predict the appearance of two different regimes: the continuously turbulent (weakly stable) boundary layer and the relatively “quiet” (very stable) boundary layer. A large number of nights (approximately 4500 in total) are analyzed using an ensemble averaging technique. The observations support the existence of these two fundamentally different regimes: weakly stable (turbulent) nights rapidly reach a steady state (within 2–3 h). In contrast, very stable nights reach a steady state much later after a transition period (2–6 h). During this period turbulence is weak and nonstationary. To characterize the regime, a new parameter is introduced: the shear capacity. This parameter compares the actual shear after sunset with the minimum shear needed to sustain continuous turbulence. In turn, the minimum shear is dictated by the heat flux demand at the surface (net radiative cooling), so that the shear capacity combines flow information with knowledge of the boundary condition. It is shown that the shear capacity enables prediction of the flow regimes. The prognostic strength of this nondimensional parameter appears to outperform the traditional ones like the similarity parameter z/L and the gradient Richardson number Ri as a regime indicator.

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