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One of the major challenges in space weather forecasting is to reliably predict the magnetic structure of interplanetary coronal mass ejections (ICMEs) in the near-Earth space.In the framework of global MHD modelling, several efforts have been made to model the CME magnetic field from Sun to Earth. However, it remains challenging to deduce a flux-rope solution that can reliably model the magnetic structure of a CME. Spheromaks are one of the models that are widely used to characterize the internal magnetic structure of a CME. However, recent studies show that spheromaks are prone toexperiencealarge rotationwhen injectedin the heliospheric domain which may affect the prediction efficacy of CME magnetic fields at 1 AU. Moreover, the fully inserted spheromaks do not have any legs attached to the Sun.In addition, due to the inherent topology of thespheromak,the in-situ signature may exhibit a double flux-rope-likeprofilenot reproduced by standard locally cylindrical flux rope models. Aiming to study thedynamics ofCMEs exhibiting different magnetic topologies, we implement a new flux-rope model in European heliospheric forecasting information asset (EUHFORIA). Our flux-rope model includes an initiallyforce-free toroidal flux-rope that is embedded in the low-coronal magnetic field. The dynamics of the flux rope in the low and middle corona is solved by a non-uniform advection constrained by the observed kinematics of the event. This results in a global non-toroidal loop-like magnetic structure that locally manifests as a cylindrical structure. At heliospheric distances, the evolution is modeled as a MHD process using EUHFORIA. As proof of concept, we use this tool to two CME events. Comparing the model results with the in-situ magnetic field configuration of the ICME at 1 au, we find that the simulated magnetic field profiles of the flux-rope are in very good agreement with the in-situ observations. Therefore, the framework of toroidal model implementation as developed in this study could prove to be a major step-forward in forecasting the geo-effectiveness of CMEs.
Sarkar et al. (Sat,) studied this question.