NDLI: Statistical Assessment of Photospheric Magnetic Features in Imminent Solar Flare Predictions

Content Provider	Springer Nature Link
Author	Song, Hui Tan, Changyi Jing, Ju Wang, Haimin Yurchyshyn, Vasyl Abramenko, Valentyna
Copyright Year	2008
Abstract	In this study we use the ordinal logistic regression method to establish a prediction model, which estimates the probability for each solar active region to produce X-, M-, or C-class flares during the next 1-day time period. The three predictive parameters are (1) the total unsigned magnetic flux T $_{flux}$, which is a measure of an active region’s size, (2) the length of the strong-gradient neutral line L $_{gnl}$, which describes the global nonpotentiality of an active region, and (3) the total magnetic dissipation E $_{diss}$, which is another proxy of an active region’s nonpotentiality. These parameters are all derived from SOHO MDI magnetograms. The ordinal response variable is the different level of solar flare magnitude. By analyzing 174 active regions, L $_{gnl}$ is proven to be the most powerful predictor, if only one predictor is chosen. Compared with the current prediction methods used by the Solar Monitor at the Solar Data Analysis Center (SDAC) and NOAA’s Space Weather Prediction Center (SWPC), the ordinal logistic model using L $_{gnl}$, T $_{flux}$, and E $_{diss}$ as predictors demonstrated its automatic functionality, simplicity, and fairly high prediction accuracy. To our knowledge, this is the first time the ordinal logistic regression model has been used in solar physics to predict solar flares.
Starting Page	101
Ending Page	125
Page Count	25
File Format	PDF
ISSN	00380938
Journal	Solar Physics
Volume Number	254
Issue Number	1
e-ISSN	1573093X
Language	English
Publisher	Springer Netherlands
Publisher Date	2008-11-28
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sun: Activity Sun: Flares Sun: Magnetic fields Magnetic observables Extraterrestrial Physics, Space Sciences Meteorology/Climatology Astrophysics
Content Type	Text
Resource Type	Article
Subject	Astronomy and Astrophysics Space and Planetary Science

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