Generation of Inflow Turbulence in Large-Eddy Simulations of Nonneutral Atmospheric Boundary Layers with the Cell Perturbation Method

Domingo Muñoz-Esparza National Center for Atmospheric Research, Boulder, Colorado

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Branko Kosović National Center for Atmospheric Research, Boulder, Colorado

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Abstract

Realistic multiscale simulations involve coupling of mesoscale and large-eddy simulation (LES) models, thus requiring efficient generation of turbulence in nested LES domains. Herein, we extend our previous work on the cell perturbation (CP) method to nonneutral atmospheric boundary layers (ABLs). A modified Richardson number scaling is proposed to determine the amplitude of the potential temperature perturbations in stable ABLs, with −1.0 overall providing optimum turbulence transition to a fully developed state (fetch reduced by a factor of 4–5, compared to the unperturbed cases). In the absence of perturbations, turbulence onset is triggered by a Kelvin–Helmholtz instability, typically occurring in the vicinity of the low-level jet maximum. It is found that a turbulent length scale can be used to more accurately estimate the optimum , where q is the turbulence kinetic energy, and N is the Brunt–Väisälä frequency. In convective ABLs, a perturbation amplitude based on mixed layer temperature variance scaling is proposed: . For that criterion to be optimum, the ratio , where is the wind speed at the top of the capping inversion, and is the convective velocity scale, needs to be incorporated: . This allows us to account for the competing roles of the surface thermal instability and the mean flow advection. For 10, the development fetch is reduced by a factor of 6, while when 3, the use of the CP method does not provide a significant advantage in the ability to generate turbulence, provided a smooth mesoscale inflow.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author: Dr. Domingo Muñoz-Esparza, domingom@ucar.edu

Abstract

Realistic multiscale simulations involve coupling of mesoscale and large-eddy simulation (LES) models, thus requiring efficient generation of turbulence in nested LES domains. Herein, we extend our previous work on the cell perturbation (CP) method to nonneutral atmospheric boundary layers (ABLs). A modified Richardson number scaling is proposed to determine the amplitude of the potential temperature perturbations in stable ABLs, with −1.0 overall providing optimum turbulence transition to a fully developed state (fetch reduced by a factor of 4–5, compared to the unperturbed cases). In the absence of perturbations, turbulence onset is triggered by a Kelvin–Helmholtz instability, typically occurring in the vicinity of the low-level jet maximum. It is found that a turbulent length scale can be used to more accurately estimate the optimum , where q is the turbulence kinetic energy, and N is the Brunt–Väisälä frequency. In convective ABLs, a perturbation amplitude based on mixed layer temperature variance scaling is proposed: . For that criterion to be optimum, the ratio , where is the wind speed at the top of the capping inversion, and is the convective velocity scale, needs to be incorporated: . This allows us to account for the competing roles of the surface thermal instability and the mean flow advection. For 10, the development fetch is reduced by a factor of 6, while when 3, the use of the CP method does not provide a significant advantage in the ability to generate turbulence, provided a smooth mesoscale inflow.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author: Dr. Domingo Muñoz-Esparza, domingom@ucar.edu
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