4.4 Article

A Blended Soundproof-to-Compressible Numerical Model for Small- to Mesoscale Atmospheric Dynamics

期刊

MONTHLY WEATHER REVIEW
卷 142, 期 12, 页码 4416-4438

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/MWR-D-13-00384.1

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  1. Deutsche Forschungsgemeinschaft through Berlin Mathematical School [KL 611/23-1,2]
  2. National Center for Atmospheric Research, Boulder, Colorado

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Ablended model for atmospheric flow simulations is introduced that enables seamless transition from fully compressible to pseudo-incompressible dynamics. The model equations are written in nonperturbation form and integrated using a well-balanced second-order finite-volume discretization. The semi-implicit scheme combines an explicit predictor for advection with elliptic corrections for the pressure field. Compressibility is implemented in the elliptic equations through a diagonal term. The compressible/pseudo-incompressible transition is realized by suitably weighting the term and provides a mechanism for removing unwanted acoustic imbalances in compressible runs. As the gradient of the pressure is used instead of the Exner pressure in the momentum equation, the influence of perturbation pressure on buoyancy must be included to ensure thermodynamic consistency. With this effect included, the thermodynamically consistent model is equivalent to Durran's original pseudo-incompressible model, which uses the Exner pressure. Numerical experiments demonstrate quadratic convergence and competitive solution quality for several benchmarks. With the inclusion of an additional buoyancy term required for thermodynamic consistency, the p-rho formulation'' of the pseudo-incompressible model closely reproduces the compressible results. The proposed unified approach offers a framework for models that are largely free of the biases that can arise when different discretizations are used. With data assimilation applications in mind, the seamless compressible/pseudo-incompressible transition mechanism is also shown to enable the flattening of acoustic imbalances in initial data for which balanced pressure distributions are unknown.

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