On the relationship between the gradient and the bulk Richardson number for the atmospheric surface layer

1992 ◽  
Vol 15 (1) ◽  
pp. 111-114 ◽  
Author(s):  
N. M. Zoumakis
Keyword(s):  
Surface Layer ◽  
Richardson Number ◽  
Atmospheric Surface Layer ◽  
Bulk Richardson Number ◽  
The Relationship

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.

Revisiting the Turbulent Prandtl Number in an Idealized Atmospheric Surface Layer

2015 ◽  
Vol 72 (6) ◽  
pp. 2394-2410 ◽  
Author(s):  
Dan Li ◽  
Gabriel G. Katul ◽  
Sergej S. Zilitinkevich
Keyword(s):  
Surface Layer ◽  
Prandtl Number ◽  
Turbulent Flows ◽  
Richardson Number ◽  
Atmospheric Surface Layer ◽  
Scaling Laws ◽  
Atmospheric Stability ◽  
Turbulent Prandtl Number ◽  
Energy Distributions ◽  
Turbulent Eddies

Abstract Cospectral budgets are used to link the kinetic and potential energy distributions of turbulent eddies, as measured by their spectra, to macroscopic relations between the turbulent Prandtl number (Prt) and atmospheric stability measures such as the stability parameter ζ, the gradient Richardson number Rg, or the flux Richardson number Rf in the atmospheric surface layer. The dependence of Prt on ζ, Rg, or Rf is shown to be primarily controlled by the ratio of Kolmogorov and Kolmogorov–Obukhov–Corrsin phenomenological constants and a constant associated with isotropization of turbulent flux production that can be independently determined using rapid distortion theory in homogeneous turbulence. Changes in scaling laws of the vertical velocity and air temperature spectra are also shown to affect the Prt–ζ (or Prt–Rg or Prt–Rf) relation. Results suggest that departure of Prt from unity under neutral conditions is induced by dissimilarity between momentum and heat in terms of Rotta constants, isotropization constants, and constants in the flux transfer terms. A maximum flux Richardson number Rfm predicted from the cospectral budgets method (=0.25) is in good agreement with values in the literature, suggesting that Rfm may be tied to the collapse of Kolmogorov spectra instead of laminarization of turbulent flows under stable stratification. The linkages between microscale energy distributions of turbulent eddies and macroscopic relations that are principally determined by dimensional considerations or similarity theories suggest that when these scalewise energy distributions of eddies experience a “transition” to other distributions (e.g., when Rf is increased over Rfm), dimensional considerations or similarity theories may fail to predict bulk flow properties.

Probability law of turbulent kinetic energy in the atmospheric surface layer

2021 ◽  
Vol 6 (7) ◽  
Author(s):  
Mohammad Allouche ◽  
Gabriel G. Katul ◽  
Jose D. Fuentes ◽  
Elie Bou-Zeid
Keyword(s):  
Surface Layer ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Atmospheric Surface Layer
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