Currently, achieving a balance between computational efficiency, accuracy, and numerical stability in coronal mass ejection (CME) simulations, particularly in the sub-Alfvénic coronal region, remains a significant challenge. This paper aims to address the challenge by integrating observational data and developing advanced numerical algorithms, focusing on reproducing large-scale CME evolutions consistent with observations in the coronal region. Based on the recently developed fully implicit thermodynamic magnetohydrodynamic (MHD) coronal model, we further use an observation-based regularized Biot–Savart law flux rope to trigger a CME event during Carrington rotation 2111. Additionally, we improve the temporal accuracy using a second-order accurate explicit singly diagonally implicit Runge–Kutta method and improve its numerical stability by applying approximate linearization in the implicitly solved intermediate stages. Furthermore, we adjust the time-evolving magnetic field to zero at the end of each physical time step to further validate the extended magnetic field decomposition approach proposed by H. P. Wang et al. It is noticed that the model successfully reproduces the CME evolution consistent with white-light coronagraph observations, enables faster-than-real-time CME propagation simulations from the solar surface to 0.1 au using only a couple hundred CPU cores, and remains numerically stable in CME simulations involving low- β regions. The simulation results show that this novel MHD coronal model, combined with an observation-based magnetic flux rope, is sufficiently numerically stable and computationally efficient to reproduce real CME events propagating through the sub-Alfvénic coronal region. Thus, this approach is well suited for practical applications in daily space weather forecasting.
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Poedts et al. (2025) studied this question.
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