Understanding the complex morphology of a CME II: how pre-eruptive conditions shape CME evolution

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Understanding the complex morphology of a CME II: how pre-eruptive conditions shape CME evolution

Authors

Abril Sahade, Cecilia Mac Cormack, Angelos Vourlidas, Teresa Nieves-Chinchilla, Cooper Downs, Clementina Sasso, Judith Karpen

Abstract

The morphology and heliospheric impact of coronal mass ejections (CMEs) are strongly shaped by their preeruptive magnetic configuration and surrounding coronal environment, yet these influences remain difficult to constrain observationally. We analyze a complex CME that erupted on 2024 October 26 using multiviewpoint remote sensing observations and in situ measurements. Using the physics based CORHELCME magnetohydrodynamic model, we test multiple physically plausible realizations of the preeruptive magnetic flux rope (MFR) and background magnetic field, using agreement with the observed evolution as a constraint on the CMEs initial state. We find that modest changes in MFR footpoint location and force balance lead to substantially different coronal trajectories, enabling rapid discrimination among candidate initial states. While several configurations reproduce the CMEs large scale propagation, realistic small scale morphology is achieved only when a near dated background magnetic field is employed. The resulting simulation reproduces key morphologies observed from three viewpoints without fine tuning, indicating that the inferred preeruptive configuration represents a robust, global solution and provides a physically consistent interpretation of their magnetic origin. Comparison with in situ shock detections highlights the role of CME solar wind interactions in shaping heliospheric signatures, though shock arrival times remain uncertain at the 11 hr level. These results demonstrate that data informed, physics based modeling can meaningfully constrain CME preeruptive conditions and bridge remote and in situ observations, while emphasizing the need for timely magnetic field measurements to improve predictive capability.

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