Development of Daily Maximum Flare-Flux Forecast Models for Strong Solar Flares

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• 作者：Seulki Shin ; Jin-Yi Lee ; Yong-Jae Moon ; Hyoungseok Chu ; Jongyeob Park
• 关键词：Flares ; forecasting ; Flares ; models
• 刊名：Solar Physics
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：291
• 期：3
• 页码：897-909
• 全文大小：1,034 KB
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• 作者单位：Seulki Shin (1)
  Jin-Yi Lee (2)
  Yong-Jae Moon (1) (2)
  Hyoungseok Chu (3)
  Jongyeob Park (1) (4)
  
  1. School of Space Research, Kyung Hee University, Yongin-Si, Korea
  2. Astronomy & Space Science, Kyung Hee University, Youngin-Si, Korea
  3. Software Policy & Research Institute, Seongnam-Si, Korea
  4. Korea Astronomy and Space Science Institute, Daejeon, Korea
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Astronomy
  Extraterrestrial Physics and Space Sciences
  Astrophysics
  
• 出版者：Springer Netherlands
• ISSN：1573-093X

文摘

We have developed a set of daily maximum flare-flux forecast models for strong flares (M- and X-class) using multiple linear regression (MLR) and artificial neural network (ANN) methods. Our input parameters are solar-activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum, and weighted total flare flux of the previous day, as well as mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classifications. For a training dataset, we used 61 events each of C-, M-, and X-class from January 1996 to December 2004. For a testing dataset, we used all events from January 2005 to November 2013. A comparison between our maximum flare-flux models and NOAA model based on true skill statistics (TSS) shows that the MLR model for X-class and the average of all flares (\(\mathrm{M}{+}\mathrm{X}\)-class) are much better than the NOAA model. According to the hitting fraction (HF), which is defined as a fraction of events satisfying the condition that the absolute differences of predicted and observed flare flux on a logarithm scale are smaller than or equal to 0.5, our models successfully forecast the maximum flare flux of about two-thirds of the events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on a daily basis for space-weather services.