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 Diagnostic Equations for the Stable Boundary Layer Height: Evaluation and Dimensional Analysis

2007 ◽  
Vol 46 (2) ◽  
pp. 212-225 ◽  
Author(s):  
G. J. Steeneveld ◽  
B. J. H. van de Wiel ◽  
A. A. M. Holtslag
Keyword(s):  
Boundary Layer ◽  
Dimensional Analysis ◽  
Stable Boundary Layer ◽  
Surface Friction ◽  
Coriolis Parameter ◽  
Layer Height ◽  
Stable Boundary ◽  
Boundary Layer Height ◽  
Stable Conditions ◽  
Surface Buoyancy

Abstract The performance of diagnostic equations for the stable boundary layer height h is evaluated with four observational datasets that represent a broad range of latitudes, land use, and surface roughness. In addition, large-eddy simulation results are used. Special care is given to data-quality selection. The diagnostic equations evaluated are so-called multilimit equations as derived by Zilitinkevich and coworkers in a number of papers. It appears that these equations show a serious negative bias, especially for h < 100 m, and it was found that the parameters involved could not be determined uniquely with calibration. As an alternative, dimensional analysis is used here to derive a formulation for h that is more robust. The formulation depends on the surface friction velocity u*, surface buoyancy flux Bs, Coriolis parameter, and the free-flow stability N. The relevance of the Coriolis parameter for the boundary layer height estimation in practice is also discussed. If the Coriolis parameter is ignored, two major regimes are found: h ∼ u*/N for weakly stable conditions and h ∼ (|Bs|/N 3)1/2 for moderate to very stable conditions.

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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