EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data-driven modelling of the evolution of CME properties in the low-corona: AR12473

Andreas Wagner, Emilia Kilpua, Daniel J. Price, Jens Pomoell, Anshu Kumari, Farhad Daei, and Ranadeep Sarkar
Andreas Wagner et al.
  • University of Helsinki, Department of Physics, Helsinki, Finland (

To better predict the impacts of solar eruptions on Earth, understanding the low-corona evolution of CMEs is crucial because this influential early phase is highly dynamic. We therefore investigate the evolution of CME properties, such as the evolution of flux rope footpoints as well as the magnetic flux enclosed in the flux rope, during this stage of the eruption. To simulate the eruption we make use of the data-driven time-dependent magnetofrictional method (TMFM), which has been proven to accurately capture a flux rope's early evolution and lift-off. We then developed a semi-automatized method for identifying the flux rope and extracting these flux ropes from 3D data cubes and tracking their evolution in time. The extraction algorithm is based on the twist parameter Tw in a 2D plane close to the polarity inversion line as a proxy for the flux rope and its temporal evolution. It is then applied to TMFM simulations of the active region AR12473, which produced an eruption on 28th of December 2015 (see e.g., Price et al, 2020). This CME was also accompanied by an M1.9 flare, that peaked at about 12:45 UT. The extracted flux rope footpoints are then compared against observational data from SDO's AIA instrument in the 1600 Å wavelength. This comparison yields a very good match with coverage parameters (see Asvestari et al, 2019) in the range of 60-70 %. The magnetic flux is extracted from the footpoints that are rooted in one specific polarity region. 

How to cite: Wagner, A., Kilpua, E., Price, D. J., Pomoell, J., Kumari, A., Daei, F., and Sarkar, R.: Data-driven modelling of the evolution of CME properties in the low-corona: AR12473, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3474,, 2022.