MCore: A non-hydrostatic atmospheric dynamical core utilizing high-order finite-volume methods

Ullrich, P. A.; Jablonowski, Christiane

doi:10.1016/j.jcp.2012.04.024

Cited by 59 publications

(62 citation statements)

References 49 publications

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“…Another important outcome of the workshop was the development of a standard test case suite and benchmark set of simulations that can be used for assessment of any future dynamical core. The test cases introduced in the 2016 workshop build on the previous DCMIP test case suites (Jablonowski et al, 2008;Ullrich et al, 2012) with tests that now incorporate simplified moist physics.…”

Section: Introductionmentioning

confidence: 99%

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

et al. 2017

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Abstract. Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere via numerical integration of the NavierStokes equations. These systems have existed in one form or another for over half of a century, with the earliest discretizations having now evolved into a complex ecosystem of algorithms and computational strategies. In essence, no two dynamical cores are alike, and their individual successes suggest that no perfect model exists. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 non-hydrostatic dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school. This review includes a choice of model grid, variable placement, vertical coordinate, prognostic equations, temporal discretization, and the diffusion, stabilization, filters, and fixers employed by each system.

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Section: Introductionmentioning

confidence: 99%

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

et al. 2017

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“…The work flow and data flow for the two algorithms is remarkably similar, which has greatly facilitated the development of the SI version. In particular, the SI version requires no special treatment at the first time step [in contrast, for example, to a Strang carryover scheme; Ullrich and Jablonowski (2012b)], and no extra fields need to be saved to restart the model.…”

Section: Comparison Of Algorithms and Communication Loadmentioning

confidence: 99%

“…Several global atmospheric models have recently been developed on quasi-uniform grids specifically for such computer architectures (Satoh et al 2008;Walko and Avissar 2008;Qaddouri and Lee 2011;Ullrich and Jablonowski 2012a;Skamarock et al 2012;Zängl et al 2015). However, because of concerns about whether a three-dimensional Helmholtz problem could be solved in an efficient and scalable way, almost all of these models retain an implicit time integration scheme only for the vertical propagation of information, combined with some form of explicit time integration scheme for the horizontal propagation of information.…”

Section: Introductionmentioning

confidence: 99%

“…As noted above, early semi-implicit schemes for atmospheric models treated only certain linear terms implicitly. Linearly implicit schemes, such as Runge-Kutta-Rosenbrock (RKR) schemes, originally described in the ODE literature, are becoming more widely applied in complex models for the solution of PDEs (e.g., Kar 2006;John et al 2006;Ullrich and Jablonowski 2012b). However, John et al (2006) found RKR schemes to be 3-4 times more expensive than a Crank-Nicolson scheme for their test cases, because the RKR linear problem must be solved accurately to ensure accuracy of the scheme overall.…”

Section: Introductionmentioning

confidence: 99%

“…However, John et al (2006) found RKR schemes to be 3-4 times more expensive than a Crank-Nicolson scheme for their test cases, because the RKR linear problem must be solved accurately to ensure accuracy of the scheme overall. Also, we carried out some initial experiments with a Strang carryover scheme (Ullrich and Jablonowski 2012b), a simple variant of RKR, but found that adding ''slow'' and ''fast'' time step contributions separately led to unacceptably large imbalances. Finally, we were motivated by the results of Cullen (2001) and Cullen and Salmond (2003) mentioned above, along with a belief that the (weakly) nonlinear problem arising from a Crank-Nicolson time step could be solved for a cost comparable to that of the corresponding linearized problem (see section 2b below).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Semi-Implicit Version of the MPAS-Atmosphere Dynamical Core

Sandbach

Thuburn

Vassilev

et al. 2015

Monthly Weather Review

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An important question for atmospheric modeling is the viability of semi-implicit time integration schemes on massively parallel computing architectures. Semi-implicit schemes can provide increased stability and accuracy. However, they require the solution of an elliptic problem at each time step, creating concerns about their parallel efficiency and scalability. Here, a semi-implicit (SI) version of the Model for Prediction Across Scales (MPAS) is developed and compared with the original model version, which uses a split Runge-Kutta (SRK3) time integration scheme. The SI scheme is based on a quasi-Newton iteration toward a Crank-Nicolson scheme. Each Newton iteration requires the solution of a Helmholtz problem; here, the Helmholtz problem is derived, and its solution using a geometric multigrid method is described. On two standard test cases, a midlatitude baroclinic wave and a small-planet nonhydrostatic gravity wave, the SI and SRK3 versions produce almost identical results. On the baroclinic wave test, the SI version can use somewhat larger time steps (about 60%) than the SRK3 version before losing stability. The SI version costs 10%-20% more per step than the SRK3 version, and the weak and strong scalability characteristics of the two versions are very similar for the processor configurations the authors have been able to test (up to 1920 processors). Because of the spatial discretization of the pressure gradient in the lowest model layer, the SI version becomes unstable in the presence of realistic orography. Some further work will be needed to demonstrate the viability of the SI scheme in this case.

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A flux‐form conservative semi‐Lagrangian multitracer transport scheme (FF‐CSLAM) for icosahedral‐hexagonal grids

Dubey

Mittal

Lauritzen

2014

J Adv Model Earth Syst

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A high-order incremental ''remap-type'' transport scheme is presented (FF-CSLAM). The scheme utilizes bi-quadratic polynomial subgrid-cell reconstruction functions based on the weighted least squares method. The integration of the reconstruction functions over flux areas, which is inherent in remap schemes, makes use of CSLAM approach of line integration. Though the formal order of the scheme is second order, yet quadratic subgrid scale polynomial reconstruction does lead to improvement in the overall accuracy of the scheme. Since the rigorous search for overlap areas between flux areas and grid cells is cumbersome, two simplifications have been suggested in the literature. The main objectives of this paper are (a) to formulate flux-form CSLAM for the icosahedral-hexagonal grid and (b) to assess the accuracy of two fluxintegral simplifications.

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MCore: A non-hydrostatic atmospheric dynamical core utilizing high-order finite-volume methods

Cited by 59 publications

References 49 publications

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models

A Semi-Implicit Version of the MPAS-Atmosphere Dynamical Core

A flux‐form conservative semi‐Lagrangian multitracer transport scheme (FF‐CSLAM) for icosahedral‐hexagonal grids

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