SWASTi-CME: A Physics-based Model to Study Coronal Mass Ejection Evolution and Its Interaction with Solar Wind

Abstract

Coronal mass ejections (CMEs) are primary drivers of space weather, and studying their evolution in the inner heliosphere is vital to prepare for a timely response. Solar wind streams, acting as background, influence their propagation in the heliosphere and associated geomagnetic storm activity. This study introduces SWASTi-CME, a newly developed MHD-based CME model integrated into the Space Weather Adaptive SimulaTion (SWASTi) framework. It incorporates a nonmagnetized elliptic cone and a magnetized flux rope CME model. To validate the model’s performance with in situ observation at L1, two Carrington rotations were chosen: one during solar maxima with multiple CMEs, and one during solar minima with a single CME. The study also presents a quantitative analysis of CME–solar wind interaction using this model. To account for ambient solar wind effects, two scenarios of different complexity in solar wind conditions were established. The results indicate that ambient conditions can significantly impact some of the CME properties in the inner heliosphere. We found that the drag force on the CME front exhibits a variable nature, resulting in asymmetric deformation of the CME leading edge. Additionally, the study reveals that the impact on the distribution of CME internal pressure primarily occurs during the initial stage, while the CME density distribution is affected throughout its propagation. Moreover, regardless of the ambient conditions, it was observed that, after a certain propagation time (t), the CME volume follows a nonfractal power-law expansion (∝t 3.03−3.33) due to the attainment of a balanced state with ambient.

Challenges in Forecasting the Evolution of a Distorted CME Observed During the First Close Solar Orbiter Perihelion

“…The origin of the CME distortion can be attributed to the presence of a faster wind generated by the equatorial coronal hole observed on the Sun in EUV light (Temmer 2021;Kay et al 2022). The interaction with a faster speed stream reduces also the drag on a CME, leading to faster speeds and shorter transit times (Kay et al 2022;Mayank et al 2023). In addition to the several remote-sensing observations, it was also possible to follow the evolution of the CME by looking at the in situ signatures from three different probes at 0.5, 0.97, and 1 au and compare the ETA at each S/C from different models.…”

Updating Measures of CME Arrival Time Errors

“…For example, X. Zhao and Dryer (2014) categorized the wide range of existing codes as empirical models (e.g., Gopalswamy et al, 2001;Kim et al, 2007;Vandas et al, 1996), expansion speed models (which are nevertheless a variant of empirical ones; Schwenn et al, 2005), drag-based models (e.g., Rollett et al, 2016;Shi et al, 2015;Vršnak et al, 2013), physics-based models (e.g., Corona-Romero et al, 2017;Hess & Zhang, 2015;Paouris & Vourlidas, 2022), and magnetohydrodynamic (MHD) models (e.g., Mayank et al, 2024;Pomoell & Poedts, 2018;Riley & Ben-Nun, 2022). In addition, more recent developments include machinelearning (ML) models (e.g., Alobaid et al, 2022;Liu et al, 2018;Y.…”