Anticipating the Geoeffectiveness of Coronal Mass Ejections

Status: Completed

Start Date: 2012-02-13

End Date: 2012-08-13

Description: Coronal Mass Ejections (CMEs) are responsible for some of the most severe space weather at Earth. Major geomagnetic storms arise when CMEs carry large amounts of magnetic flux as they propagate in the solar wind. If these magnetic fields have a southward orientation (oppositely directed to the magnetic field at the Earth's magnetopause), they can cause a geomagnetic storm. Predicting in advance whether observed CMEs will carry geoeffective magnetic fields is a long-term priority for the CCMC at NASA GFSC and other groups within NASA as well. We propose to combine the existing CORHEL (Corona-Heliosphere) model of the solar corona and solar wind with a robust technique for generating simulated CMEs. When successfully completed, the new tool, CORHEL-CG, will allow routine simulation of CMEs and represent a leap forward in physics-based space weather prediction models.
Benefits: The CCMC, located at NASA GSFC, is presently running the WSA-Enlil model in real time, and is testing the inclusion of cone model CMEs. While this model represents important advance in that physics-based models are now being used to forecast CMEs, it cannot predict the nature of the magnetic fields embedded in the CME. When successfully completed, CORHEL-CG will be able to overcome this limitation and may provide significantly better descriptions of CME in interplanetary space. This should not only be useful to the CCMC, but to other groups at NASA as well.

CMEs are of concern not only to NASA, but to many government and commercial entities dependent on satellites and aircraft. For example, NOAA SWPC is transitioning the WSA-Enlil-Cone model to operations to provide warnings of CME impacts. The Air Force is also interested in predicting and mitigating the impacts of geoeffective CMEs. Once we have successfully developed CORHEL-CG for NASA applications, we can adapt it to address the needs of these customers as well.

Lead Organization: Predictive Science, Inc.