TY - JOUR
T1 - Statistical assessment of photospheric magnetic features in imminent solar flare predictions
AU - Song, Hui
AU - Tan, Changyi
AU - Jing, Ju
AU - Wang, Haimin
AU - Yurchyshyn, Vasyl
AU - Abramenko, Valentyna
N1 - Funding Information:
Acknowledgements The authors are grateful to the referee for valuable comments and suggestions. They also thank Dr. T.E. Berger for providing the method to correct the underestimation problem of MDI. SOHO is a project of international cooperation between NASA and ESA. This work is supported by NSF under Grant Nos. IIS-0324816, ATM-0548952, ATM-0342560, and ATM-0536921 and by NASA under Grant No. NNG0-6GC81G. VY’s additional support is from NASA Grant Nos. NNG0-5GN34G and NASA ACE NNG0-4GJ51G.
PY - 2009/1
Y1 - 2009/1
N2 - 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.
AB - 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.
KW - Magnetic observables
KW - Sun: Activity
KW - Sun: Flares
KW - Sun: Magnetic fields
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U2 - 10.1007/s11207-008-9288-3
DO - 10.1007/s11207-008-9288-3
M3 - Article
AN - SCOPUS:57849161901
SN - 0038-0938
VL - 254
SP - 101
EP - 125
JO - Solar Physics
JF - Solar Physics
IS - 1
ER -