Prediction of Coronal Mass Ejections from Vector Magnetograms: Quantitative Measures as Predictors

Astronomy and Astrophysics – Astrophysics

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7500 Solar Physics, Astrophysics, And Astronomy, 7509 Corona, 7513 Coronal Mass Ejections, 7524 Magnetic Fields, 7531 Prominence Eruptions

Scientific paper

In a pilot study of 4 active regions (Falconer, D.A. 2001, JGR, in press), we derived two quantitative measures of an active region's global nonpotentiality from the region's vector magnetogram, 1) the net current (IN), and 2) the length of the strong-shear, strong-field main neutral line (LSS), and used these two measures of the CME productivity of the active regions. We compared the global nonpotentiality measures to the active regions' CME productivity determined from GOES and Yohkoh/SXT observations. We found that two of the active regions were highly globally nonpotential and were CME productive, while the other two active regions had little global nonpotentiality and produced no CMEs. At the Fall 2000 AGU (Falconer, Moore, & Gary, 2000, EOS 81, 48 F998), we reported on an expanded study (12 active regions and 17 magnetograms) in which we evaluated four quantitative global measures of an active region's magnetic field and compared these measures with the CME productivity. The four global measures (all derived from MSFC vector magnetograms) included our two previous measures (IN and LSS) as well as two new ones, the total magnetic flux (Φ ) (a measure of an active region's size), and the normalized twist (α =μ IN/Φ ). We found that the three measures of global nonpotentiality (IN, LSS, α ) were all well correlated (>99% confidence level) with an active region's CME productivity within (2 days of the day of the magnetogram. We will now report on our findings of how good our quantitative measures are as predictors of active-region CME productivity, using only CMEs that occurred after the magnetogram. We report the preliminary skill test of these quantitative measures as predictors. We compare the CME prediction success of our quantitative measures to the CME prediction success based on an active region's past CME productivity. We examine the cases of the handful of false positive and false negatives to look for improvements to our predictors. This work is funded by NSF through the Space Weather Program and by NASA through the Solar Physics Supporting Research and Technology Program.

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