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HERMES: Highly Efficient and quasi-Realistic Modeling of coronal mass Ejections in a time-evolving Solar-terrestrial background

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To enable timely action in mitigating damage from severe space weather events, there is an urgent need for advanced Sun-to-Earth MHD models capable of delivering timely, high-fidelity, and comprehensive space weather forecasts. Recently, the numerical stability of the time-evolving coronal MHD models COCONUT and SIP-IFVM have been significantly improved by the energy decomposition strategy and the extended magnetic field decomposition methods, respectively. The implicit temporal integrations, with Newton iterations or pseudo–time marching method performed within each time step, enables high computational efficiency with desired temporal accuracy. Several observation-based coronal evolution and CME propagation simulations further demonstrate that these methods collaboratively achieve an effective balance between high computational efficiency, numerical stability, and modeling accuracy. Currently, we further go to the planetary space by directly extending our coronal models to 1 AU or coupling the coronal model with an inner heliosphere model. Based on the faster-than-real-time time-evolving solar-terrestrial MHD model, we are performing CME propagation simulations in the time-evolving solar-terrestrial plasma background, rather than the usually adopted quasi-static background. We will report on the algorithm innovations we recently made for improving the performance of MHD coronal models and CME simulations. We will also discuss the impact of temporal variations in the coronal and solar wind background on CME propagation, as well as the effects of the interface introduced by coupling separately run coronal and inner heliosphere models, a common practice adopted to simplify parameter adjustment and reduce computational cost. These algorithmic innovations and resulting findings provide an opportunity to develop more reliable Sun-to-Earth MHD models suitable for practical CME simulations.
Title: HERMES: Highly Efficient and quasi-Realistic Modeling of coronal mass Ejections in a time-evolving Solar-terrestrial background
Description:
To enable timely action in mitigating damage from severe space weather events, there is an urgent need for advanced Sun-to-Earth MHD models capable of delivering timely, high-fidelity, and comprehensive space weather forecasts.
Recently, the numerical stability of the time-evolving coronal MHD models COCONUT and SIP-IFVM have been significantly improved by the energy decomposition strategy and the extended magnetic field decomposition methods, respectively.
The implicit temporal integrations, with Newton iterations or pseudo–time marching method performed within each time step, enables high computational efficiency with desired temporal accuracy.
Several observation-based coronal evolution and CME propagation simulations further demonstrate that these methods collaboratively achieve an effective balance between high computational efficiency, numerical stability, and modeling accuracy.
Currently, we further go to the planetary space by directly extending our coronal models to 1 AU or coupling the coronal model with an inner heliosphere model.
Based on the faster-than-real-time time-evolving solar-terrestrial MHD model, we are performing CME propagation simulations in the time-evolving solar-terrestrial plasma background, rather than the usually adopted quasi-static background.
We will report on the algorithm innovations we recently made for improving the performance of MHD coronal models and CME simulations.
We will also discuss the impact of temporal variations in the coronal and solar wind background on CME propagation, as well as the effects of the interface introduced by coupling separately run coronal and inner heliosphere models, a common practice adopted to simplify parameter adjustment and reduce computational cost.
These algorithmic innovations and resulting findings provide an opportunity to develop more reliable Sun-to-Earth MHD models suitable for practical CME simulations.

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