Statistical Assessment of Photospheric Magnetic Features in Imminent Solar Flare Predictions

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.