On numerical realizability of thermal convection

Piotrowski, Zbigniew; Smolarkiewicz, Piotr K.; Malinowski, Szymon P.; Wyszogrodzki, Andrzej A.

doi:10.1016/j.jcp.2009.05.023

Cited by 61 publications

(63 citation statements)

References 73 publications

(149 reference statements)

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“…Both benchmarks represent viable elements of nonhydrostatic dynamics affecting predictability of natural weather at larger meso and synoptics scales. For instance, dissipative effects within the planetary boundary layer are important for developing convective structures aloft [20], the interaction of which with deep gravity wave modes in the free troposphere can modulate the organization of the cloud clusters at larger scales [10,1]. The finite-amplitude effects related to topographically forced stably stratified flows, such as wave breaking and downslope windstorms or more generally mountain-wave induced turbulence, present obvious challenge for mesoscale weather predictability [5].…”

Section: Resultsmentioning

confidence: 99%

“…The anelastic equations (1)-(3) assume nonrotating Boussinesq limit with constant reference profiles Θ o (z) = Θ 0 and ρ o (z) = ρ 0 . The growth of the convective boundary layer is driven by a prescribed diabatic source in (2), D Θ = −dH/dz, with a heat flux specified as Because these diabatic/viscous forcings quickly decay with height, they only parametrize nearsurface effects; whereas subgrid-scale modeling aloft is delegated to dissipative properties of MPDATA [23,20].…”

Section: Convective Boundary Layermentioning

confidence: 99%

“…1 and 2 by joining barycenters, faces and edges of the primary mesh prisms surrounding vertices "i" and "j". Figure 3 displays the instantaneous vertical velocity field at the end of the Run T, organized into characteristic, albeit irregular, RayleighBénard cells [20]. The convective scales for the three runs are collected in Table 1 and compared with Runs E and I of [18] that correspond to calculations with excluded/included explicit subgrid-scale model; i.e, ILES versus LES.…”

Section: Convective Boundary Layermentioning

confidence: 99%

See 2 more Smart Citations

An unstructured-mesh atmospheric model for nonhydrostatic dynamics

Smolarkiewicz

Szmelter

Wyszogrodzki

2013

Journal of Computational Physics

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Section: Resultsmentioning

confidence: 99%

Section: Convective Boundary Layermentioning

confidence: 99%

Section: Convective Boundary Layermentioning

confidence: 99%

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An unstructured-mesh atmospheric model for nonhydrostatic dynamics

Smolarkiewicz

Szmelter

Wyszogrodzki

2013

Journal of Computational Physics

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“…EULAG experience garnered over a wide range of hydrodynamical multiscale flow simulations indicate that its underlying advective scheme operates as adaptive subgrid model, turning on wherever and whenever dissipation is required to maintain stability, and remaining minimally dissipative otherwise (Smolarkiewicz & Margolin 2007;Piotrowski et al 2009). Moreover, when it activates, dissipation is concentrated at the smallest scales resolved by the spatial mesh.…”

Section: The Simulationmentioning

confidence: 99%

Characteristics of magnetic solar-like cycles in a 3D MHD simulation of solar convection

Passos

Charbonneau

2014

A&A

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We analyse the statistical properties of the stable magnetic cycle unfolding in an extended 3D magnetohydrodynamic simulation of solar convection produced with the EULAG-MHD code. The millennium simulation spans over 1650 years, in the course of which forty polarity reversals take place on a regular ∼40 yr cadence, remaining well-synchronized across solar hemispheres. In order to characterize this cycle and facilitate its comparison with measures typically used to represent solar activity, we build two proxies for the magnetic field in the simulation mimicking the solar toroidal field and the polar radial field. Several quantities that characterize the cycle are measured (period, amplitudes, etc.) and correlations between them are computed. These are then compared with their observational analogs. From the typical Gnevyshev-Ohl pattern, to hints of Gleissberg modulation, the simulated cycles share many of the characteristics of their observational analogs even though the simulation lacks poloidal field regeneration through active region decay, a mechanism nowadays often considered an essential component of the solar dynamo. Some significant discrepancies are also identified, most notably the in-phase variation of the simulated poloidal and toroidal large-scale magnetic components, and the low degree of hemispheric coupling at the level of hemispheric cycle amplitudes. Possible causes underlying these discrepancies are discussed.

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“…(1a), (1b) and (1d) represent sub-grid (turbulent) dissipation of momentum and diffusion of heat, with the symbols τ , h and j denoting the deviatoric stress tensor, the heat flux vector and the scalar diffusion flux vector, respectively. Here, the latter are modelled by means of an eddy viscosity/diffusivity assumption with dissipation/diffusion coefficients proportional to the square root of the prognostic "turbulent kinetic energy" (TKE) (Schumann, 1991;Piotrowski et al, 2009). The prognostic equations of the governing set (1) can be written in a generic conservation law form…”

Section: Eulag-lcmmentioning

confidence: 99%

Dimension of aircraft exhaust plumes at cruise conditions: effect of wake vortices

et al. 2014

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Abstract. The dispersion of aircraft emissions during the vortex phase is studied using a 3-D LES model with Lagrangian particle tracking. The simulations start with a fully rolled-up vortex pair of a type B747/A340 airplane and the tracer centred around the vortex cores. The tracer dilution and plume extent is studied for a variety of ambient and aircraft parameters until aircraft-induced effects have ceased. For typical upper tropospheric conditions, the impact of stratification is more dominant compared to turbulence intensity or vertical wind shear. Moreover, the sensitivity to the initial tracer distribution was found to be weak. Along the transverse direction, the tracer concentrations can be well approximated by a Gaussian distribution, along the vertical a superposition of three Gaussian distributions is adequate. For the studied parameter range, the vertical plume expansion ranges from 400 m to 550 m and cross-sectional area from 4.0 × 10 4 m 2 to 6 × 10 4 m 2 after six minutes. For validation, selected simulations were compared to an alternative LES model and to in-situ NO-measurements.

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On numerical realizability of thermal convection

Cited by 61 publications

References 73 publications

An unstructured-mesh atmospheric model for nonhydrostatic dynamics

An unstructured-mesh atmospheric model for nonhydrostatic dynamics

Characteristics of magnetic solar-like cycles in a 3D MHD simulation of solar convection

Dimension of aircraft exhaust plumes at cruise conditions: effect of wake vortices

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