Simulating Non-hydrostatic atmospheres on Planets (SNAP): formulation, validation and application to the Jovian atmosphere. (arXiv:1901.02955v1 [astro-ph.EP])

<a href="http://arxiv.org/find/astro-ph/1/au:+Li_C/0/1/0/all/0/1">Cheng Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chen_X/0/1/0/all/0/1">Xi Chen</a>

A new non-hydrostatic and cloud-resolving atmospheric model is developed for

studying moist convection and cloud formation in planetary atmospheres. It is

built on top of the Athena++ framework, utilizing its static/adaptive

mesh-refinement, parallelization, curvilinear geometry, and dynamic task

scheduling. We extend the original hydrodynamic solver to vapors, clouds, and

precipitation. Microphysics is formulated generically so that it can be applied

to both Earth and Jovian planets. We implemented the Low Mach number

Approximate Riemann Solver (LMARS) for simulating low speed atmospheric flows

in addition to the usual Roe and HLLC Riemann solvers. Coupled with a

fifth-order Weighted Essentially Nonoscillatory (WENO) subgrid-reconstruction

method, the sharpness of critical fields such as clouds is well-preserved, and

no extra hyperviscosity or spatial filter is needed to stabilize the model.

Unlike many atmospheric models, total energy is used as the prognostic variable

of the thermodynamic equation. One significant advantage of using total energy

as a prognostic variable is that the entropy production due to irreversible

mixing process can be properly captured. The model is designed to provide a

unified framework for exploring planetary atmospheres across various

conditions, both terrestrial and Jovian. First, a series of standard numerical

tests for Earth’s atmosphere is carried out to demonstrate the performance and

robustness of the new model. Second, simulation of an idealized Jovian

atmosphere in radiative-convective equilibrium shows that 1) the temperature

gradient is superadiabatic near the water condensation level because of the

changing of the mean molecular weight, and 2) the mean profile of ammonia gas

shows a depletion in the subcloud layer down to nearly 10 bars. Relevance to

the recent Juno observations is discussed.

A new non-hydrostatic and cloud-resolving atmospheric model is developed for

studying moist convection and cloud formation in planetary atmospheres. It is

built on top of the Athena++ framework, utilizing its static/adaptive

mesh-refinement, parallelization, curvilinear geometry, and dynamic task

scheduling. We extend the original hydrodynamic solver to vapors, clouds, and

precipitation. Microphysics is formulated generically so that it can be applied

to both Earth and Jovian planets. We implemented the Low Mach number

Approximate Riemann Solver (LMARS) for simulating low speed atmospheric flows

in addition to the usual Roe and HLLC Riemann solvers. Coupled with a

fifth-order Weighted Essentially Nonoscillatory (WENO) subgrid-reconstruction

method, the sharpness of critical fields such as clouds is well-preserved, and

no extra hyperviscosity or spatial filter is needed to stabilize the model.

Unlike many atmospheric models, total energy is used as the prognostic variable

of the thermodynamic equation. One significant advantage of using total energy

as a prognostic variable is that the entropy production due to irreversible

mixing process can be properly captured. The model is designed to provide a

unified framework for exploring planetary atmospheres across various

conditions, both terrestrial and Jovian. First, a series of standard numerical

tests for Earth’s atmosphere is carried out to demonstrate the performance and

robustness of the new model. Second, simulation of an idealized Jovian

atmosphere in radiative-convective equilibrium shows that 1) the temperature

gradient is superadiabatic near the water condensation level because of the

changing of the mean molecular weight, and 2) the mean profile of ammonia gas

shows a depletion in the subcloud layer down to nearly 10 bars. Relevance to

the recent Juno observations is discussed.

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