Coronal Mass Ejections from Sun to Earth: New Modeling and Statistical Approaches
Thursday, 14 February 2019: 11:30
Fountain I/II (Westin Pasadena)
Anna V Malanushenko1, Sarah E Gibson1, Kévin Dalmasse2, Viacheslav G Merkin3, Elena Provornikova4, Angelos Vourlidas4, Charles Nickolos Arge5, Douglas W Nychka6, Michael James Wiltberger1 and Natasha Flyer1, (1)High Altitude Observatory, UCAR, Boulder, CO, United States, (2)Institut de Recherche en Astrophysique et Planétologie, Toulouse, France, (3)The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (4)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)Colorado School of Mines, Golden, CO, United States
Abstract:
Solar coronal mass ejections (CMEs) are violent eruptive phenomena which originate on the Sun; their heliospheric extensions, called interplanetary CMEs, are known for their potential to impact the whole heliosphere and, in particular, the Earth. While not all CMEs are launched in such a way as to hit the Earth, those that do can have big impacts on Earth's magnetosphere. The magnitude of such impact depends upon many factors such as the CME launch location and velocity, its positioning within the background solar wind, its mass, and its magnetic properties such as the orientation of its front with respect to the Earth's magnetic field.
Case studies of how iCMEs propagate through the heliosphere are complicated by many factors, including often incomplete input for models. We present and discuss a different approach. Rather than focusing on modeling a particular event, we intend to carry out a large statistical study in the event parameter space. Further, Bayesian statistics will be used along with large statistical databases of near-Sun and near-Earth observables, to infer statistical distributions of relevant CME input parameters, which are capable of yielding given distributions of observables, for a given stage of the the solar cycle.
We use a analytical flux rope model (Gibson&Low model) and a background solar wind boundary (Wang-Sheeley-Arge model) as inputs for a new MHD heliospheric simulation code (Gamera). We discuss the details of the coupling and show early modeling results. We also experiment with convolutional neural networks to investigate the extend to which machine learning can be used to help predict the outcome of the modeling.
