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Title: A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer

Abstract

Abstract In convective flows, vertical turbulent fluxes, covariances between vertical velocity and scalar thermodynamic variables, include contributions from local mixing and large-scale coherent motions, such as updrafts and downdrafts. The relative contribution of these motions to the covariance is important in turbulence parameterizations. However, the flux partition is challenging, especially in regions without convective cloud. A method to decompose the vertical flux based on the corresponding joint probability density function (JPD) is introduced. The JPD-based method partitions the full JPD into a joint Gaussian part and the complement, which represent the local mixing and the large-scale coherent motions, respectively. The coherent part can be further divided into updraft and downdraft parts based on the sign of vertical velocity. The flow decomposition is independent of water condensate (cloud) and can be applied in cloud-free convection, the subcloud layer, and stratiform cloud regions. The method is applied to large-eddy simulation model data of three boundary layers. The results are compared with traditional cloud and cloud-core decompositions and a decaying scalar conditional sampling method. The JPD-based method includes a single free parameter and sensitivity tests show weak dependence on the parameter values. The results of the JPD-based method are somewhat similar to themore » cloud-core and conditional sampling methods. However, differences in the relative magnitude of the flux decomposition terms suggest that an objective definition of the flow regions is subtle and diagnosed flow properties like updraft characteristics depend on the sampling method. Moreover, the flux decomposition depends on the thermodynamic variable and convection characteristics.« less

Authors:
 [1];  [2];  [3]
  1. Faculdade de Ciências, Instituto Dom Luiz, Universidade de Lisboa, Lisbon, Portugal, and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  2. Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut
  3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1420365
Resource Type:
Published Article
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Name: Monthly Weather Review Journal Volume: 146 Journal Issue: 2; Journal ID: ISSN 0027-0644
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Chinita, Maria J., Matheou, Georgios, and Teixeira, João. A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer. United States: N. p., 2018. Web. doi:10.1175/MWR-D-17-0166.1.
Chinita, Maria J., Matheou, Georgios, & Teixeira, João. A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer. United States. doi:10.1175/MWR-D-17-0166.1.
Chinita, Maria J., Matheou, Georgios, and Teixeira, João. Tue . "A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer". United States. doi:10.1175/MWR-D-17-0166.1.
@article{osti_1420365,
title = {A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer},
author = {Chinita, Maria J. and Matheou, Georgios and Teixeira, João},
abstractNote = {Abstract In convective flows, vertical turbulent fluxes, covariances between vertical velocity and scalar thermodynamic variables, include contributions from local mixing and large-scale coherent motions, such as updrafts and downdrafts. The relative contribution of these motions to the covariance is important in turbulence parameterizations. However, the flux partition is challenging, especially in regions without convective cloud. A method to decompose the vertical flux based on the corresponding joint probability density function (JPD) is introduced. The JPD-based method partitions the full JPD into a joint Gaussian part and the complement, which represent the local mixing and the large-scale coherent motions, respectively. The coherent part can be further divided into updraft and downdraft parts based on the sign of vertical velocity. The flow decomposition is independent of water condensate (cloud) and can be applied in cloud-free convection, the subcloud layer, and stratiform cloud regions. The method is applied to large-eddy simulation model data of three boundary layers. The results are compared with traditional cloud and cloud-core decompositions and a decaying scalar conditional sampling method. The JPD-based method includes a single free parameter and sensitivity tests show weak dependence on the parameter values. The results of the JPD-based method are somewhat similar to the cloud-core and conditional sampling methods. However, differences in the relative magnitude of the flux decomposition terms suggest that an objective definition of the flow regions is subtle and diagnosed flow properties like updraft characteristics depend on the sampling method. Moreover, the flux decomposition depends on the thermodynamic variable and convection characteristics.},
doi = {10.1175/MWR-D-17-0166.1},
journal = {Monthly Weather Review},
number = 2,
volume = 146,
place = {United States},
year = {2018},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1175/MWR-D-17-0166.1

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Cited by: 4 works
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