Observation of the temperature structure function parameter, C T 2, over the ocean

1978 ◽  
Vol 15 (4) ◽  
pp. 507-523 ◽  
Author(s):  
K. L. Davidson ◽  
T. M. Houlihan ◽  
C. W. Fairall ◽  
G. E. Schacher
Keyword(s):  
Structure Function ◽  
Function Parameter ◽  
Temperature Structure

scholarly journals On the Relationship between the TKE Dissipation Rate and the Temperature Structure Function Parameter in the Convective Boundary Layer

2020 ◽  
Vol 77 (7) ◽  
pp. 2311-2326
Author(s):  
Hubert Luce ◽  
Lakshmi Kantha ◽  
Hiroyuki Hashiguchi ◽  
Abhiram Doddi ◽  
Dale Lawrence ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Structure Function ◽  
Convective Boundary Layer ◽  
Dissipation Rate ◽  
Function Parameter ◽  
Mixing Efficiency ◽  
Buoyancy Frequency ◽  
Temperature Structure ◽  
Convective Boundary

AbstractUnder stably stratified conditions, the dissipation rate ε of turbulence kinetic energy (TKE) is related to the structure function parameter for temperature , through the buoyancy frequency and the so-called mixing efficiency. A similar relationship does not exist for convective turbulence. In this paper, we propose an analytical expression relating ε and in the convective boundary layer (CBL), by taking into account the effects of nonlocal heat transport under convective conditions using the Deardorff countergradient model. Measurements using unmanned aerial vehicles (UAVs) equipped with high-frequency response sensors to measure velocity and temperature fluctuations obtained during the two field campaigns conducted at Shigaraki MU observatory in June 2016 and 2017 are used to test this relationship between ε and in the CBL. The selection of CBL cases for analysis was aided by auxiliary measurements from additional sensors (mainly radars), and these are described. Comparison with earlier results in the literature suggests that the proposed relationship works, if the countergradient term γD in the Deardorff model, which is proportional to the ratio of the variances of potential temperature θ and vertical velocity w, is evaluated from in situ (airplane and UAV) observational data, but fails if evaluated from large-eddy simulation (LES) results. This appears to be caused by the tendency of the variance of θ in the upper part of the CBL and at the bottom of the entrainment zone to be underestimated by LES relative to in situ measurements from UAVs and aircraft. We discuss this anomaly and explore reasons for it.

scholarly journals Comparison of Direct and Spectral Methods for Evaluation of the Temperature Structure Parameter in Numerically Simulated Convective Boundary Layer Flows

2016 ◽  
Vol 144 (6) ◽  
pp. 2205-2214 ◽  
Author(s):  
Jeremy A. Gibbs ◽  
Evgeni Fedorovich ◽  
Björn Maronga ◽  
Charlotte Wainwright ◽  
Manuel Dröse
Keyword(s):  
Boundary Layer ◽  
Structure Function ◽  
Spectral Method ◽  
Convective Boundary Layer ◽  
Direct Method ◽  
Potential Temperature ◽  
Function Parameter ◽  
Temperature Structure ◽  
Structure Parameter ◽  
Convective Boundary

Abstract In many engineering and meteorological applications, atmospheric turbulence within the planetary boundary layer is described in terms of its representative parameters. One such parameter is the structure-function (or structure) parameter that is used to characterize the intensity of turbulent fluctuations of atmospheric flow variables. Structure parameters are derivatives of structure functions, but are used more frequently than the latter ones for practical needs as they do not explicitly include dependence on the separation distance. The structure parameter of potential temperature, which is the subject of this study, describes the spatial variability of the temperature fluctuations. It is broadly represented in theories and models of electromagnetic and acoustic wave propagation in the atmosphere, and forms the basis for the scintillometer measurement concept. The authors consider three methods to compute the potential temperature structure function and structure parameter: the direct method, the true spectral method, and the conventional spectral method. Each method is tested on high-resolution potential temperature datasets generated from large-eddy simulations of a variety of convective boundary layer flow cases reproduced by two representative numerical codes. Results indicate that the popular conventional spectral method routinely exaggerates the potential temperature structure-function parameter, likely due to the unrealistic assumptions underlying the method. The direct method and true spectral method are recommended as the more suitable approaches.

Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation

2015 ◽  
Vol 155 (2) ◽  
pp. 189-208 ◽  
Author(s):  
Charlotte E. Wainwright ◽  
Timothy A. Bonin ◽  
Phillip B. Chilson ◽  
Jeremy A. Gibbs ◽  
Evgeni Fedorovich ◽  
...  
Keyword(s):  
Large Eddy Simulation ◽  
Structure Function ◽  
Function Parameter ◽  
Unmanned Aerial Systems ◽  
Temperature Structure ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Aerial Systems

scholarly journals N-terminus oligomerization is conserved in intracellular calcium release channels

2014 ◽  
Vol 459 (2) ◽  
pp. 265-273 ◽  
Author(s):  
Spyros Zissimopoulos ◽  
Jason Marsh ◽  
Laurence Stannard ◽  
Monika Seidel ◽  
F. Anthony Lai
Keyword(s):  
Structure Function ◽  
Intracellular Calcium ◽  
Calcium Release ◽  
Function Parameter ◽  
Calcium Release Channels ◽  
Cellular Processes ◽  
N Terminus ◽  
Intracellular Calcium Release ◽  
Ca2 Channels

Intracellular Ca2+ channels are of paramount importance for numerous cellular processes. In the present paper we report on a novel N-terminus intersubunit interaction, an essential structure–function parameter, which is conserved in both families of intracellular Ca2+ channels.

Experimental determination of the effective structure-function parameter for atmospheric turbulence

1999 ◽  
Vol 105 (2) ◽  
pp. 912-914 ◽  
Author(s):  
D. K. Wilson ◽  
D. I. Havelock ◽  
M. Heyd ◽  
M. J. Smith ◽  
J. M. Noble ◽  
...  
Keyword(s):  
Structure Function ◽  
Experimental Determination ◽  
Atmospheric Turbulence ◽  
Function Parameter

An empirical expression for on the axis of a slightly heated turbulent round jet

2019 ◽  
Vol 867 ◽  
pp. 392-413 ◽  
Author(s):  
J. Lemay ◽  
L. Djenidi ◽  
R. A. Antonia ◽  
A. Benaïssa
Keyword(s):  
Structure Function ◽  
Dissipation Rate ◽  
Velocity Structure ◽  
Power Laws ◽  
Second Order ◽  
Temperature Structure ◽  
Analytical Expression ◽  
Round Jet ◽  
Mean Temperature ◽  
The Mean

Self-preservation analyses of the equations for the mean temperature and the second-order temperature structure function on the axis of a slightly heated turbulent round jet are exploited in an attempt to develop an analytical expression for$\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D703}}$, the mean dissipation rate of$\overline{\unicode[STIX]{x1D703}^{2}}/2$, where$\overline{\unicode[STIX]{x1D703}^{2}}$is the temperature variance. The analytical approach follows that of Thiessetet al.(J. Fluid Mech., vol. 748, 2014, R2) who developed an expression for$\unicode[STIX]{x1D716}_{k}$, the mean turbulent kinetic energy dissipation rate, using the transport equation for$\overline{(\unicode[STIX]{x1D6FF}u)^{2}}$, the second-order velocity structure function. Experimental data show that complete self-preservation for all scales of motion is very well satisfied along the jet axis for streamwise distances larger than approximately 30 times the nozzle diameter. This validation of the analytical results is of particular interest as it provides justification and confidence in the analytical derivation of power laws representing the streamwise evolution of different physical quantities along the axis, such as:$\unicode[STIX]{x1D702}$,$\unicode[STIX]{x1D706}$,$\unicode[STIX]{x1D706}_{\unicode[STIX]{x1D703}}$,$R_{U}$,$R_{\unicode[STIX]{x1D6E9}}$(all representing characteristic length scales), the mean temperature excess$\unicode[STIX]{x1D6E9}_{0}$, the mixed velocity–temperature moments$\overline{u\unicode[STIX]{x1D703}^{2}}$,$\overline{v\unicode[STIX]{x1D703}^{2}}$and$\overline{\unicode[STIX]{x1D703}^{2}}$and$\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D703}}$. Simple models are proposed for$\overline{u\unicode[STIX]{x1D703}^{2}}$and$\overline{v\unicode[STIX]{x1D703}^{2}}$in order to derive an analytical expression for$A_{\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D703}}}$, the prefactor of the power law describing the streamwise evolution of$\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D703}}$. Further, expressions are also derived for the turbulent Péclet number and the thermal-to-mechanical time scale ratio. These expressions involve global parameters that are most likely to be influenced by the initial and/or boundary conditions and are therefore expected to be flow dependent.

scholarly journals Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 384 ◽  
Author(s):  
Hubert Luce ◽  
Lakshmi Kantha ◽  
Hiroyuki Hashiguchi ◽  
Dale Lawrence
Keyword(s):  
Richardson Number ◽  
Layer Structure ◽  
Lower Troposphere ◽  
Energy Dissipation Rate ◽  
Function Parameter ◽  
Mixing Efficiency ◽  
Temperature Structure ◽  
Frequency Spectra ◽  
Kolmogorov Turbulence ◽  
Turbulence Parameters

Turbulence parameters in the lower troposphere (up to ~4.5 km) are estimated from measurements of high-resolution and fast-response cold-wire temperature and Pitot tube velocity from sensors onboard DataHawk Unmanned Aerial Vehicles (UAVs) operated at the Shigaraki Middle and Upper atmosphere (MU) Observatory during two ShUREX (Shigaraki UAV Radar Experiment) campaigns in 2016 and 2017. The practical processing methods used for estimating turbulence kinetic energy dissipation rate ε and temperature structure function parameter C T 2 from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (−5/3) subranges in respective spectra. Using a formulation relating ε and C T 2 valid for Kolmogorov turbulence in steady state, the flux Richardson number R f and the mixing efficiency χ m are then estimated. The statistical analysis confirms the variability of R f and χ m around ~ 0.13 − 0.14 and ~ 0.16 − 0.17 , respectively, values close to the canonical values found from some earlier experimental and theoretical studies of both the atmosphere and the oceans. The relevance of the interpretation of the inertial subranges in terms of Kolmogorov turbulence is confirmed by assessing the consistency of additional parameters, the Ozmidov length scale L O , the buoyancy Reynolds number R e b , and the gradient Richardson number Ri. Finally, a case study is presented showing altitude differences between the peaks of N 2 , C T 2 and ε , suggesting turbulent stirring at the margin of a stable temperature gradient sheet. The possible contribution of this sheet and layer structure on clear air radar backscattering mechanisms is examined.

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