The dependence of the bulk Richardson number on stability in the surface layer

1991 ◽  
Vol 57 (4) ◽  
pp. 407-414 ◽  
Author(s):  
N. M. Zoumakis ◽  
A. G. Kelessis
Keyword(s):  
Surface Layer ◽  
Richardson Number ◽  
Bulk Richardson Number

Evaluation of methods for estimating atmospheric stability at two coastal sites

2018 ◽  
Vol 42 (6) ◽  
pp. 561-575 ◽  
Author(s):  
Lars Morten Bardal ◽  
Anja Eide Onstad ◽  
Lars Roar Sætran ◽  
John Amund Lund
Keyword(s):  
Surface Layer ◽  
Richardson Number ◽  
Atmospheric Stability ◽  
Stability Conditions ◽  
Bulk Richardson Number ◽  
Wind Speed Profile ◽  
Flux Measurements ◽  
Vertical Wind Speed ◽  
Stability Measurements ◽  
Evaluation Of Methods

Understanding the atmospheric stability conditions is important in order to obtain accurate estimates of the vertical wind speed profile. This work compares and evaluates common methods for estimation of atmospheric stability using standard meteorological mast observations. Atmospheric stability distributions from three different met-masts located at two coastal sites are calculated and compared. The atmospheric stability parameter, L is estimated using the bulk Richardson number, the surface-layer Richardson number, and calculated directly from eddy covariance flux measurements. The resulting distributions vary depending on which method is used. The atmospheric stability measurements from two masts located 3 km apart in similar terrain are compared directly. The highest correlation is found for the surface-layer Richardson number method. This method it also less sensitive to variation of measurement heights than the bulk Richardson number method.

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.

Mixing events in a stratified jet subject to surface wind and buoyancy forcing

2011 ◽  
Vol 685 ◽  
pp. 54-82 ◽  
Author(s):  
Hieu T. Pham ◽  
Sutanu Sarkar
Keyword(s):  
Surface Layer ◽  
Wind Stress ◽  
Richardson Number ◽  
Reynolds Shear Stress ◽  
Surface Wind ◽  
Initial Density ◽  
Shear Instability ◽  
Energy Budgets ◽  
Surface Cooling ◽  
Gradient Richardson Number

AbstractThe fine-scale response of a subsurface stable stratified jet subject to the forcing of surface wind stress and surface cooling is investigated using direct numerical simulation. The initial velocity profile consists of a symmetric jet located below a surface layer driven by a constant wind stress. The initial density profile is well-mixed in the surface layer and linearly stratified in both upper and lower flanks of the jet. The minimum value of the gradient Richardson number in the upper flank of the jet exceeds the critical value of 0.25 for linear shear instability. Broadband finite-amplitude fluctuations are introduced to the surface layer to initiate the simulation. Turbulence is generated in the surface layer and deepens into the jet upper flank. Internal waves generated by the turbulent surface layer are observed to propagate downward across the jet. The momentum flux carried by the waves is significantly smaller than the Reynolds shear stress extracted from the background velocity. The wave energy flux is also smaller than the turbulence production by mean shear. Ejections of fluid parcels by horseshoe-like vortices cause intermittent patches of intense dissipation inside the jet upper flank where the background gradient Richardson number is larger than 0.25. Drag due to the wind stress is smaller than the drag caused by turbulent stress in the flow. Analysis of the mean and turbulent kinetic energy budgets suggests that the energy input by surface forcing is considerably smaller than the energy extracted from the initially imposed background shear in the surface layer.

Large Eddy Simulations of a Stratified Shear Layer

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Hieu T. Pham ◽  
Sutanu Sarkar
Keyword(s):  
Reynolds Number ◽  
Shear Layer ◽  
Richardson Number ◽  
Scalar Dissipation ◽  
Momentum Thickness ◽  
Bulk Richardson Number ◽  
Scalar Flux ◽  
Linear Evolution ◽  
Eddy Simulation ◽  
Large Eddy

The performance of the large eddy simulation (LES) approach in predicting the evolution of a shear layer in the presence of stratification is evaluated. The LES uses a dynamic procedure to compute subgrid model coefficients based on filtered velocity and density fields. Two simulations at different Reynolds numbers are simulated on the same computational grid. The fine LES simulated at a low Reynolds number produces excellent agreement with direct numerical simulations (DNS): the linear evolution of momentum thickness and bulk Richardson number followed by an asymptotic approach to constant values is correctly represented and the evolution of the integrated turbulent kinetic energy budget is well captured. The model coefficients computed from the velocity and the density fields are similar and have a value in range of 0.01-0.02. The coarse LES simulated at a higher Reynolds number Re = 50,000 shows acceptable results in terms of the bulk characteristics of the shear layer, such as momentum thickness and bulk Richardson number. Analysis of the turbulent budgets shows that, while the subgrid stress is able to remove sufficient energy from the resolved velocity fields, the subgrid scalar flux and thereby the subgrid scalar dissipation are underestimated by the model.

scholarly journals Physics of Stratocumulus Top (POST): turbulence characteristics

2016 ◽  
Vol 16 (15) ◽  
pp. 9711-9725 ◽  
Author(s):  
Imai Jen-La Plante ◽  
Yongfeng Ma ◽  
Katarzyna Nurowska ◽  
Hermann Gerber ◽  
Djamal Khelif ◽  
...  
Keyword(s):  
Dissipation Rate ◽  
Richardson Number ◽  
Reynolds Numbers ◽  
Bulk Richardson Number ◽  
Velocity Fluctuations ◽  
Turbulence Characteristics ◽  
Tke Dissipation Rate ◽  
Thermodynamic Variables ◽  
Large Eddies ◽  
Flight Measurements

Abstract. Turbulence observed during the Physics of Stratocumulus Top (POST) research campaign is analyzed. Using in-flight measurements of dynamic and thermodynamic variables at the interface between the stratocumulus cloud top and free troposphere, the cloud top region is classified into sublayers, and the thicknesses of these sublayers are estimated. The data are used to calculate turbulence characteristics, including the bulk Richardson number, mean-square velocity fluctuations, turbulence kinetic energy (TKE), TKE dissipation rate, and Corrsin, Ozmidov and Kolmogorov scales. A comparison of these properties among different sublayers indicates that the entrainment interfacial layer consists of two significantly different sublayers: the turbulent inversion sublayer (TISL) and the moist, yet hydrostatically stable, cloud top mixing sublayer (CTMSL). Both sublayers are marginally turbulent, i.e., the bulk Richardson number across the layers is critical. This means that turbulence is produced by shear and damped by buoyancy such that the sublayer thicknesses adapt to temperature and wind variations across them. Turbulence in both sublayers is anisotropic, with Corrsin and Ozmidov scales as small as  ∼  0.3 and  ∼  3 m in the TISL and CTMSL, respectively. These values are  ∼  60 and  ∼  15 times smaller than typical layer depths, indicating flattened large eddies and suggesting no direct mixing of cloud top and free-tropospheric air. Also, small scales of turbulence are different in sublayers as indicated by the corresponding values of Kolmogorov scales and buoyant and shear Reynolds numbers.

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