Determination of turbulent momentum and heat fluxes by spectral methods

Kader, B. A.

doi:10.1007/bf00119096

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…The method proved to be well suited for flux measurements above the ocean. Many investigators [e.g., Kader, 1992;Dyer and Hicks, 1982;Champagne et al, 1977] have reported that the dissipation method agreed well with the measurements.…”

Section: Introductionmentioning

confidence: 74%

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Hsieh¹,

Katul²,

Schieldge³

et al. 1996

Water Resources Research

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Abstract. Dissipation and flux-variance methods, derived from the turbulent kinetic energy and temperature variance budget equations in conjunction with Monin-Obukov similarity theory, were used to estimate surface fluxes of momentum and sensible heat. To examine the performance of these two methods, direct eddy correlation measurements were carried out above a nonuniform grass-covered forest clearing in Durham, North Carolina. The dissipation method sensible heat flux predictions were in good agreement with eddy correlation measurements. Also, the flux-variance method reproduced the measured sensible heat flux well following an adjustment to the similarity constant. However, the momentum flux (or friction velocity) estimated by the dissipation and fluxvariance methods were both inferior to those for sensible heat flux. The data from this experiment indicated that the above two methods are sensitive to the dimensionless wind shear ( m ) and temperature standard deviation ( ) functions. On the basis of dimensional analysis and the temperature variance budget equation a new dissipation approach for estimating sensible heat flux was derived. The similarity constant for this new approach was shown to be around 1.6 for uniform surfaces and from the data of this experiment.

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Section: Introductionmentioning

confidence: 74%

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Hsieh¹,

Katul²,

Schieldge³

et al. 1996

Water Resources Research

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“…The modified WC71 follows the data more closely when compared with PDR2 and FV01. For the convective case, Albertson et al (1997) proposed a three-sublayer model (hereafter APKE97a) based on directional dimensional analysis following Kader and Yaglom (1990), and Kader (1992):…”

Section: The Dimensionless Turbulent Dissipationmentioning

confidence: 99%

Local Imbalance of Turbulent Kinetic Energy in the Surface Layer

Zimmerman

Princevac

2008

Boundary-Layer Meteorol

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We utilize experimental data collected in 2002 over an open field in Hanford, Washington, USA, to investigate the turbulent kinetic energy (TKE) budget in the atmospheric surface layer. The von Kármán constant was determined from the near-neutral wind profiles to be 0.36 ± 0.02 rather than the classical value of 0.4. The TKE budget was normalized and all terms were parameterized as functions of a stability parameter z/L, where z is the distance from the ground and L is the Obukhov length. The shear production followed the Businger-Dyer relation for −2 < z/L < 1. Contrary to the traditional Monin-Obukhov similarity theory (MOST), the shear, buoyancy and dissipation terms were found to be imbalanced due to a non-zero vertical transport over all stabilities. Motivated by this local imbalance, modified parameterizations of the dissipation and the turbulent transport were attempted and generated good agreement with the experimental data. Assuming stationarity and horizontal homogeneity, the pressure transport was estimated from the residual of the TKE budget.

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“…This fact may be a consequence of the influence of the singularities that occur in near-neutral regimes for the tracers and were not neglected by these authors. Note that the formulations proposed by Kader (1992) need to be multiplied by the von Kármán constant in order to compare to our results.…”

Section: Dissipation and Residual Components In The Variance Budget Ementioning

confidence: 92%

“…They proposed to divide the observed data into three wind regimes: 3 − 7, 10 − 12 and 17 − 20 m s −1 , respectively. However, the BLLAST datasets fall only within the first recommended range, which emphasizes the need for considering more relevant mechanisms rather than only ζ. eterizations proposed by Kader (1992) and Ohtaki (1985) cannot be considered linear and increase rapidly in magnitude. This fact may be a consequence of the influence of the singularities that occur in near-neutral regimes for the tracers and were not neglected by these authors.…”

Section: Dissipation and Residual Components In The Variance Budget Ementioning

confidence: 99%

Similarity analysis of turbulent transport and dissipation for momentum, temperature, moisture, and CO<sub>2</sub> during BLLAST

Hackerott

Paskyabi

Kral

et al. 2016

Preprint

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Determination of turbulent momentum and heat fluxes by spectral methods

Cited by 22 publications

References 19 publications

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Local Imbalance of Turbulent Kinetic Energy in the Surface Layer

Similarity analysis of turbulent transport and dissipation for momentum, temperature, moisture, and CO<sub>2</sub> during BLLAST

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Determination of turbulent momentum and heat fluxes by spectral methods

Cited by 22 publications

References 19 publications

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Estimation of Momentum and Heat Fluxes Using Dissipation and Flux‐Variance Methods in the Unstable Surface Layer

Local Imbalance of Turbulent Kinetic Energy in the Surface Layer

Similarity analysis of turbulent transport and dissipation for momentum, temperature, moisture, and CO&lt;sub&gt;2&lt;/sub&gt; during BLLAST

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Similarity analysis of turbulent transport and dissipation for momentum, temperature, moisture, and CO<sub>2</sub> during BLLAST