Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Prediction

Mukhopadhyay, Agnit; Jia, Xianzhe; Welling, D. T.; Liemohn, Michael W.

doi:10.1002/essoar.10505113.1

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…These configurations result in ∼1 million grid cells with a near-body resolution of 1/4 R E and ∼2 million grid cells with 1/8 R E maximum resolution, respectively. While capable of running faster than real time on a modest number (about 100) of CPU cores, the operational SWMF/Geospace models can well reproduce large-scale features such as cross polar cap potential (CPCP) and Dst (Haiducek et al, 2017;Mukhopadhyay et al, 2021), and can also predict local ground magnetic perturbations with skill scores of practical value (Pulkkinen et al, 2013;Toth et al, 2014).…”

Section: Swmf/geospace Configurationmentioning

confidence: 99%

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Gombosi

Chen

Glocer

et al. 2021

J. Space Weather Space Clim.

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MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the ``free spirit'' approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).

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Section: Swmf/geospace Configurationmentioning

confidence: 99%