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as large-eddy simulation (LES). In principle, LES uses sufficiently small grid resolution to represent the largest and most energetic features in a turbulent flow. LES requires a subgrid turbulence model, which accounts for the effects of unresolved turbulence on resolved-scale fields. Subgrid models for LES are often designed based on theoretical conditions [e.g., chapter 6 of Wyngaard (2010) ]. However, the underlying assumption of LES is that resolved fluctuations contain most of the
as large-eddy simulation (LES). In principle, LES uses sufficiently small grid resolution to represent the largest and most energetic features in a turbulent flow. LES requires a subgrid turbulence model, which accounts for the effects of unresolved turbulence on resolved-scale fields. Subgrid models for LES are often designed based on theoretical conditions [e.g., chapter 6 of Wyngaard (2010) ]. However, the underlying assumption of LES is that resolved fluctuations contain most of the
direct numerical simulation (DNS) or large-eddy simulation (LES) codes. In DNS studies, the Reynolds number (Re) is usually substantially smaller than that for a typical atmospheric boundary layer flow (Re ~ 10 5 –10 10 ) as a result of the limitation in computing power [e.g., Re is equal to 1000 in Coleman (1999) and 5200 in Lee and Moser (2015) ]. LES codes have problems resolving processes near the ground surface, where the scale of dominant eddies, comparable to the vertical distance from the
direct numerical simulation (DNS) or large-eddy simulation (LES) codes. In DNS studies, the Reynolds number (Re) is usually substantially smaller than that for a typical atmospheric boundary layer flow (Re ~ 10 5 –10 10 ) as a result of the limitation in computing power [e.g., Re is equal to 1000 in Coleman (1999) and 5200 in Lee and Moser (2015) ]. LES codes have problems resolving processes near the ground surface, where the scale of dominant eddies, comparable to the vertical distance from the
1. Introduction This study presents a series of large-eddy simulations (LES) employing the particle-based and probabilistic approach for representing aerosol, cloud, and warm-rain microphysics introduced in Shima (2008) and Shima et al. (2009) and referred to as the superdroplet method (SDM). The simulations cover a 24-h-long evolution of a field of shallow convective clouds typical of the trade wind boundary layer. The highlight of the paper is the discussion of the simulation results in
1. Introduction This study presents a series of large-eddy simulations (LES) employing the particle-based and probabilistic approach for representing aerosol, cloud, and warm-rain microphysics introduced in Shima (2008) and Shima et al. (2009) and referred to as the superdroplet method (SDM). The simulations cover a 24-h-long evolution of a field of shallow convective clouds typical of the trade wind boundary layer. The highlight of the paper is the discussion of the simulation results in
, evaporation, and mixing. To do this, we use a Lagrangian parcel-tracking model (LPTM) coupled with a large-eddy simulation (LES). The LPTM predicts the trajectories of air parcels and diagnoses their velocities and thermodynamic properties by spatial interpolation from the grid of the host model. As Heus et al. (2008) discussed, an LPTM tracks many massless tracer parcels that follow the simulated flow, and these parcels are uniquely identifiable by their trajectories. In this approach, an LPTM can
, evaporation, and mixing. To do this, we use a Lagrangian parcel-tracking model (LPTM) coupled with a large-eddy simulation (LES). The LPTM predicts the trajectories of air parcels and diagnoses their velocities and thermodynamic properties by spatial interpolation from the grid of the host model. As Heus et al. (2008) discussed, an LPTM tracks many massless tracer parcels that follow the simulated flow, and these parcels are uniquely identifiable by their trajectories. In this approach, an LPTM can
explain the enhanced level of VKE. Here, large-eddy simulations (LESs) are used to develop accurate scalings for the VKE enhancement under realistic wind and wave forcing. a. The Craik–Leibovich mechanism and Langmuir turbulence In the CL mechanism, Langmuir circulations arise from the interaction of the Stokes drift u S of surface waves and wave-averaged currents driven by a surface stress τ 0 = | τ 0 | = ρ w u * 2 , where u * is the friction velocity in water of density ρ w (see Thorpe 2004
explain the enhanced level of VKE. Here, large-eddy simulations (LESs) are used to develop accurate scalings for the VKE enhancement under realistic wind and wave forcing. a. The Craik–Leibovich mechanism and Langmuir turbulence In the CL mechanism, Langmuir circulations arise from the interaction of the Stokes drift u S of surface waves and wave-averaged currents driven by a surface stress τ 0 = | τ 0 | = ρ w u * 2 , where u * is the friction velocity in water of density ρ w (see Thorpe 2004
1. Introduction Large-eddy simulations of atmospheric boundary layer flows rely heavily on the quality of the chosen turbulence closure scheme because of limited grid resolution and density-stratification effects. This is especially true for neutrally or stably stratified flows, where the contribution of the turbulence model dominates that of the resolved terms (see the discussions in Sullivan et al. 1994 ; Kosović 1997 ). The evaluation of turbulence closure models for large-eddy simulation
1. Introduction Large-eddy simulations of atmospheric boundary layer flows rely heavily on the quality of the chosen turbulence closure scheme because of limited grid resolution and density-stratification effects. This is especially true for neutrally or stably stratified flows, where the contribution of the turbulence model dominates that of the resolved terms (see the discussions in Sullivan et al. 1994 ; Kosović 1997 ). The evaluation of turbulence closure models for large-eddy simulation
some previous large-eddy simulations (LESs) have been unsatisfactory in one important manner: in the boundary layer flow swirling into the tornado core, the effects of turbulence have been almost entirely represented by a subgrid-scale (SGS) mixing scheme, rather than by resolved turbulent eddies. This is not entirely due to insufficiently small grid spacings. Since the far-field environment in most such simulations is relatively quiescent, and usually even less well resolved because of grid
some previous large-eddy simulations (LESs) have been unsatisfactory in one important manner: in the boundary layer flow swirling into the tornado core, the effects of turbulence have been almost entirely represented by a subgrid-scale (SGS) mixing scheme, rather than by resolved turbulent eddies. This is not entirely due to insufficiently small grid spacings. Since the far-field environment in most such simulations is relatively quiescent, and usually even less well resolved because of grid
1. Introduction The immense range of scales encountered in many flows of practical importance render direct numerical simulation (DNS) of the Navier–Stokes equations prohibitively expensive. In the atmospheric boundary layer, for instance, the largest eddies that scale with the boundary layer height are six orders of magnitude larger than the smallest Kolmogorov scales (e.g., Wyngaard 2010 ). By contrast, only a meager fraction of this dynamical range can be captured, even if we take into
1. Introduction The immense range of scales encountered in many flows of practical importance render direct numerical simulation (DNS) of the Navier–Stokes equations prohibitively expensive. In the atmospheric boundary layer, for instance, the largest eddies that scale with the boundary layer height are six orders of magnitude larger than the smallest Kolmogorov scales (e.g., Wyngaard 2010 ). By contrast, only a meager fraction of this dynamical range can be captured, even if we take into
an instrumented tower. To investigate some of the outlined issues, we have developed a time series sodar simulator. It uses data from a numerical large-eddy simulation (LES) that generates a realistic turbulent flow field in the ABL. The simulator is designed such that the scanning parameters can be set to match different commercially available sodars. An additional benefit of the simulator is that it allows for repeated scanning of the same atmospheric fields using different scanning parameters
an instrumented tower. To investigate some of the outlined issues, we have developed a time series sodar simulator. It uses data from a numerical large-eddy simulation (LES) that generates a realistic turbulent flow field in the ABL. The simulator is designed such that the scanning parameters can be set to match different commercially available sodars. An additional benefit of the simulator is that it allows for repeated scanning of the same atmospheric fields using different scanning parameters
features of the stratocumulus to shallow cumulus transition in a set of simple well-characterized large-eddy simulations (LESs). Specifically, we choose SST as the main parameter and perform a series of three-dimensional LESs; each simulation is assigned a different but fixed-in-time SST and is run until statistically steady. This approach simplifies the investigation of cloud response without the inherent time-lag effects in a transition case setup with time-dependent SST and large-scale forcings. The
features of the stratocumulus to shallow cumulus transition in a set of simple well-characterized large-eddy simulations (LESs). Specifically, we choose SST as the main parameter and perform a series of three-dimensional LESs; each simulation is assigned a different but fixed-in-time SST and is run until statistically steady. This approach simplifies the investigation of cloud response without the inherent time-lag effects in a transition case setup with time-dependent SST and large-scale forcings. The
