JpGU-AGU Joint Meeting 2017

Presentation information

[EE] Oral

P (Space and Planetary Sciences) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM18] [EE] Origin of Earth-affecting Coronal Mass Ejections

Thu. May 25, 2017 1:45 PM - 3:15 PM A01 (Tokyo Bay Makuhari Hall)

convener:No? Lugaz(University of New Hampshire Main Campus), Kanya Kusano(Institute for Space-Earth Environmental Research, Nagoya University), Neel P Savani(NASA GSFC / University of Maryland Baltimore County), Ayumi Asai(Astronomical Observatory, Kyoto University), Chairperson:Neel Savani(NASA GSFC / UMBC), Chairperson:Ayumi Asai(Astronomical Observatory, Kyoto University)

2:45 PM - 3:00 PM

[PEM18-10] Using Interplanatery Scintillation Data to Improve Ensemble Modeling of Coronal Mass Ejections

*Aleksandre Taktakishvili1,2, Leila Mays2, P. K. Manoharan3, Lutz Rastaetter2, Maria Kuznetsova2 (1.Catholic University of America, Washington, DC, USA, 2.NASA Goddard Space Flight Center, Greenbelt, MD, USA, 3.Ooty Radio Telescope, Bangalore, India)

Keywords:Coronal Mass Ejections, Ensemble Modeling, Interplanetary Scintillation Observations

The impact of the coronal mass ejections (CMEs) on the Earth’s magnetosphere-ionosphere system can cause widespread anomalies for satellites from geosynchronous to low-Earth orbit and produce effects such as geomagnetically induced currents. At the NASA/GSFC Community Coordinated Modeling Center we have been using ensemble modeling of CMEs since 2012. In this work we use interplanetary scintillation (IPS) observations from the Ooty Radio Telescope facility in India to track CMEs and compare to an ensemble of CME forecasts. This allowes downselection of the ensemble members and helps to improve forecasting of CME arrival times. The inclusion of observations of the solar wind density and velocity using IPS from hundreds of distant sources in ensemble modeling of CMEs can be a game-changing improvement of the current state of the art in CME forecasting. Moreover, the same method can be applied to ensemble simulations of the magnetosphere that suffer from the same scarcity of input data with similar degree of uncertainty.