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Creators/Authors contains: "Chow, Fotini Katopodes"

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  1. Abstract

    Kilometer‐scale grid spacing is increasingly being used in regional numerical weather prediction and climate simulation. This resolution range is in the terra incognita, where energetic eddies are partially resolved and turbulence parameterization is a challenge. The Smagorinsky and turbulence kinetic energy 1.5‐order models are commonly used at this resolution range, but, as traditional eddy‐diffusivity models, they can only represent forward‐scattering turbulence (downgradient fluxes), whereas the dynamic reconstruction model (DRM), based on explicit filtering, permits countergradient fluxes. Here we perform large‐eddy simulation of deep convection with 100‐m horizontal grid spacing and use these results to evaluate the performance of turbulence schemes at 1‐km horizontal resolution. The Smagorinsky and turbulence kinetic energy 1.5 schemes produce large‐amplitude errors at 1‐km resolution, due to excessively large eddy diffusivities attributable to the formulation of the squared moist Brunt‐Väisälä frequency (). With this formulation in cloudy regions, eddy diffusivity can be excessively increased in “unstable” regions, which produce downward (downgradient) heat flux in a conditionally unstable environment leading to destabilization and further amplification of eddy diffusivities. A more appropriate criterion based on saturation mixing ratio helps eliminate this problem. However, shallow clouds cannot be simulated well in any case at 1‐km resolution with the traditional models, whereas DRM allows for countergradient heat flux for both shallow and deep convection and predicts the distribution of clouds and fluxes satisfactorily. This is because DRM employs an eddy diffusivity model that is dynamically adjusted and a reconstruction approach that allows countergradient fluxes.

     
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  2. Abstract

    Turbulence parameterization plays a critical role in the simulation of many weather regimes. For challenging cases such as the stratocumulus-capped boundary layer (SCBL), traditional schemes can produce unrealistic results even when a fine large-eddy-simulation (LES) resolution is used. Here we present an implicit generalized linear algebraic subfilter-scale model (iGLASS) to better represent unresolved turbulence in the simulation of the atmospheric boundary layer, at both standard LES and so-called terra incognita (TI) resolutions. The latter refers to a range of model resolutions where turbulent eddies are only partially resolved, and therefore the simulated processes are sensitive to the representation of unresolved turbulence. iGLASS is based on the truncated conservation equations of subfilter-scale (SFS) fluxes, but it integrates the full equations of the SFS turbulence kinetic energy and potential energy to retain “memory” of the SFS turbulence. Our evaluations suggest iGLASS can perform significantly better than traditional eddy-diffusivity models and exhibit skills comparable to the dynamic reconstruction model (DRM). For a neutral boundary layer case run at LES resolution, the simulation using iGLASS exhibits a wind profile that reasonably matches the similarity-theory solution. For an SCBL case with 5-m vertical resolution, iGLASS maintains more realistic cloud water profiles and boundary layer structure than traditional schemes. The SCBL case is also tested at TI resolution, and iGLASS also exhibits superior performance. iGLASS permits significant backscatter, whereas traditional models allow forward scatter (diffusion) only. As a physics-based approach, iGLASS appears to be a viable alternative for turbulence parameterization.

     
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