Statistical assessment of photospheric magnetic features in imminent solar flare predictions

Hui Song, Changyi Tan, Ju Jing, Haimin Wang, Vasyl Yurchyshyn, Valentyna Abramenko

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)101-125
Number of pages25
JournalSolar Physics
Issue number1
StatePublished - Jan 2009

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


  • Magnetic observables
  • Sun: Activity
  • Sun: Flares
  • Sun: Magnetic fields


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