Scaling the Decay of Turbulence Kinetic Energy in the Free-Convective Boundary Layer

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El Guernaoui, O.; Reuder, J.; Esau, I.; Wolf, T.; Maronga, B.: Scaling the Decay of Turbulence Kinetic Energy in the Free-Convective Boundary Layer. In: Boundary-Layer Meteorology 173 (2019), Nr. 1, S. 79-97. DOI: https://doi.org/10.1007/s10546-019-00458-z

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/5230

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We investigate the scaling for decaying turbulence kinetic energy (TKE) in the free-convective boundary layer, from the time the surface heat flux starts decaying, until a few hours after it has vanished. We conduct a set of large-eddy simulation experiments, consider various initial convective situations, and prescribe realistic decays of the surface heat flux over a wide range of time scales. We find that the TKE time evolution is dictated by the decaying magnitude of the surface heat flux up to 0.7 τ approximately, where τ is the prescribed duration from maximum to zero surface heat flux. During the time period starting at zero surface heat flux, we search for potential power-law scaling by examining the log–log presentation of TKE as a function of time. First, we find that the description of the decay highly depends on whether the time origin is defined as the time when the surface heat flux starts decaying (traditional scaling framework), or the time when it vanishes (proposed new scaling framework). Second, when varying τ, the results plotted in the traditional scaling framework indicate variations in the power-law decay rates over several orders of magnitude. In the new scaling framework, however, we find a unique decay exponent in the order of 1, independent of the initial convective condition, and independent of τ, giving support for the proposed scaling framework.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Fakultät für Mathematik und Physik

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1 image of flag of Germany Germany 29 70.73%
2 image of flag of China China 3 7.32%
3 image of flag of United States United States 2 4.88%
4 image of flag of No geo information available No geo information available 1 2.44%
5 image of flag of Morocco Morocco 1 2.44%
6 image of flag of Latvia Latvia 1 2.44%
7 image of flag of Italy Italy 1 2.44%
8 image of flag of India India 1 2.44%
9 image of flag of Hong Kong Hong Kong 1 2.44%
10 image of flag of France France 1 2.44%

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