Astronomy and Astrophysics – Astrophysics
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsh43a1810f&link_type=abstract
American Geophysical Union, Fall Meeting 2010, abstract #SH43A-1810
Astronomy and Astrophysics
Astrophysics
[2139] Interplanetary Physics / Interplanetary Shocks, [6225] Planetary Sciences: Solar System Objects / Mars, [7513] Solar Physics, Astrophysics, And Astronomy / Coronal Mass Ejections, [7924] Space Weather / Forecasting
Scientific paper
We perform a survey of the longitudinal extent and general nature of fast Coronal Mass Ejections (CMEs) identifiable at both Mars and Earth and use the ENLIL Magneto-Hydro-Dynamic model to replicate data for the CMEs at both planets. We have chosen 18 fast CMEs with initial velocities, according to the SOHO/LASCO catalogue, between 1200 and 2700 km/s in the period 2001-2003. CME arrival is identified at Earth by sharp increases in velocity, density and magnetic field strength in data compiled from the ACE,WIND and Geotail spacecraft, while CME arrival at Mars is identified by a large increase in magnetic field strength and by increases in the background countrate of the Electron Reflectometer (ER) instrument on Mars Global Surveyor (MGS). The ER backgrounds are used to determine the presence of Solar Energetic Particles related to fast CMEs. We run ENLIL using two different sets of input parameters, both estimated from SOHO/LASCO images, but one set is estimated manually and one set is estimated using an automated method. Specific parameters of interest are arrival time, longitudinal span, and propagation direction of the CMEs. At Earth the velocity, density and magnetic field strength are compared to Omni data, while at Mars the solar wind dynamic pressure is compared to an upstream pressure proxy estimated from magnetic field data from MGS. A qualitative estimate of the usefulness of ENLIL in CME modeling will be given, as well as a comparison of the effectiveness of the two methods used to find input parameters for the model.
Brain David Andrew
Delory Gregory T.
Falkenberg T. V.
Mitchell Dana D.
Pulkkinen Aappo
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