Automatic Prediction of Super Geomagnetic Storms and Solar Flares

Details Automatic Prediction of Super Geomagnetic Storms and Solar Flares Automatic Prediction of Super Geomagnetic Storms and Solar Flares

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007aas...21010501s&link_type=abstract

American Astronomical Society Meeting 210, #105.01; Bulletin of the American Astronomical Society, Vol. 39, p.230

Mathematics

Probability

Scientific paper

In this study we first investigated the relationship between magnetic structures of coronal mass ejection (CME) source regions and geomagnetic storms, in particular, the super storms when the Dst index decreases below -200 nT. By examining all full halo CMEs that erupted between 1996 and 2004, we selected 73 events associated with M-class and X-class solar flares, which have a clearly identifiable source region. By analyzing daily full-disk MDI magnetograms, we found that the horizontal gradient of the line-of-sight magnetic field is a viable parameter to identify a flaring magnetic neutral line and thus can be used to predict the possible source region of CMEs. We report that for about 92% of super storms the orientation angle of the magnetic structures of source regions was found to be southward. Our findings demonstrate that this approach can be used to perform an automatic prediction of the occurrence of large X-class flares and super geomagnetic storms.
In order to further extend our prediction of flares, we next used the ordinal logistic regression method to establish a prediction model, which estimates the probability for each active region to produce X-, M- or C-class flares during the next 1-day time period. Three predictive parameters are: total unsigned magnetic flux Tflux, the length of strong gradient neutral line Lgnl, and total magnetic dissipation Ediss. The ordinal response variable is the different level of solar flares magnitude. Compared the results with the current predictions methods used by NASA SDAC and NOAA SEC, the ordinal logistic model using Lgnl and Tflux as predictors demonstrated its automaticity, simpleness and fairly high prediction accuracy. To our knowledge, this is the first time the ordinal logistic regression model was used in solar physics to predict solar flares.

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