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George H. Bryan
,
Nathan A. Dahl
,
David S. Nolan
, and
Richard Rotunno

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

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Qingfang Jiang
,
Shouping Wang
, and
Peter Sullivan

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

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Sylwester Arabas
and
Shin-ichiro Shima

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

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Takanobu Yamaguchi
and
David A Randall

, 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

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Ramsey R. Harcourt
and
Eric A. D’Asaro

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

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Fotini Katopodes Chow
and
Robert L. Street

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

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David S. Nolan
,
Nathan A. Dahl
,
George H. Bryan
, and
Richard Rotunno

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

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Rachael N. Cross
,
David J. Bodine
,
Robert D. Palmer
,
Casey Griffin
,
Boonleng Cheong
,
Sebastian Torres
,
Caleb Fulton
,
Javier Lujan
, and
Takashi Maruyama

radar variables. Using large-eddy simulations (LESs) and a dual-polarization radar simulator (SimRadar; Cheong et al. 2017 ), the evolution of the TDS is examined across a simulated tornado life cycle using physically based models. While several TDS hypotheses have been proposed ( Table 1 ), many of these hypotheses are largely speculative because of incomplete information about debris or tornadic wind speeds. Using simulations allows for the direct comparison of tornado-scale wind speeds with

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Daniel Chung
and
Georgios Matheou

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

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Takanobu Yamaguchi
and
David A. Randall

ratio, L is the latent heat, and where p is the pressure. Previous studies provide evidence both for and against the existence and importance of CTEI. Some numerical studies, performed in the early 1980s, supported the CTEI hypothesis; for example, Deardorff (1980) simulated cloud breakup, possibly due to CTEI, in a low-resolution large-eddy simulation (LES). CTEI may also play a role in the cloud breakup simulated by Moeng and Arakawa (1980) , who used a second-order closure model with a

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