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Izvestiya, Atmospheric and Oceanic Physics

, Volume 55, Issue 4, pp 312–323 | Cite as

Layered Structure of Stably Stratified Turbulent Shear Flows

  • A. V. GlazunovEmail author
  • E. V. Mortikov
  • K. V. Barskov
  • E. V. Kadantsev
  • S. S. Zilitinkevich
Article
  • 27 Downloads

Abstract

Data of a numerical simulation of a stably stratified turbulent Couette flow are analyzed for various values of the Richardson number. Two different methods are used: direct numerical simulation (DNS) and large-eddy simulation (LES). It is shown that the flow contains large organized structures, along with chaotic turbulence, regardless of the simulation method. These structures appear as inclined layers in the temperature field with weakly stable stratification, separated by very thin layers with large temperature gradients. The existence of such layered structures in nature is indirectly confirmed by the analysis of data from field measurements on the meteorological mast, where temperature gradient histograms are found to be far from the normal distribution and similar to temperature gradient probability distributions obtained in numerical model data. The simulations indicate an increase in the turbulent Prandtl number with an increase in the gradient Richardson number. It is likely that the identified structures serve as efficient barriers for vertical turbulent heat flux without blocking the momentum transfer. We propose a hypothesis that it is these structures which serve as a physical mechanism for maintaining turbulence under supercritically stable stratification.

Keywords:

atmospheric boundary layer (ABL) direct numerical simulation (DNS) large-eddy simulation (LES) direct measurements of turbulence self-organized structures wind shear turbulent Prandtl number turbulence stable stratification 

Notes

FUNDING

This study was carried out at the Research Computing Center, Moscow State University, and supported by the Russian Science Foundation (grant no. 17-17- 01210). Data collection and processing (Section 3 and, in part, 4) were supported by the Russian Science Foundation (grant no. 15-17-20009). The development of numerical models and numerical experiments (Sections 2.1 and 2.1) were supported by the Russian Foundation for Basic Research (grants nos. 16-05-01094, 18-05-60126, 18-05-60299) and by Academy of Finland project ClimEco no. 314 798/799 (2018-2020).

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Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • A. V. Glazunov
    • 1
    • 3
    Email author
  • E. V. Mortikov
    • 1
    • 3
  • K. V. Barskov
    • 1
    • 2
  • E. V. Kadantsev
    • 5
  • S. S. Zilitinkevich
    • 4
    • 5
  1. 1.Research Computing Center, Moscow State UniversityMoscowRussia
  2. 2.Obukhov Institute of Atmospheric Physics, Russian Academy of SciencesMoscowRussia
  3. 3.Marchuk Institute of Numerical Mathematics, Russian Academy of SciencesMoscowRussia
  4. 4.Finnish Meteorological InstituteHelsinkiFinland
  5. 5.Institute for Atmospheric and Earth System Research, University of HelsinkiHelsinkiFinland

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