Solar Physics

, 293:100 | Cite as

Nowcasting Solar Energetic Particle Events Using Principal Component Analysis

  • A. PapaioannouEmail author
  • A. Anastasiadis
  • A. Kouloumvakos
  • M. Paassilta
  • R. Vainio
  • E. Valtonen
  • A. Belov
  • E. Eroshenko
  • M. Abunina
  • A. Abunin


We perform a principal component analysis (PCA) on a set of six solar variables (i.e. width/size (\(s\)) and velocity (\(u\)) of a coronal mass ejection, logarithm of the solar flare (SF) magnitude (\(\log\mathit{SXRs}\)), SF longitude (\(\mathit{lon}\)), duration (\(\mathit{DT}\)), and rise time (\(\mathit{RT}\))). We classify the solar energetic particle (SEP) event radiation impact (in terms of the National Oceanic and Atmospheric Administration scales) with respect to the characteristics of their parent solar events. We further attempt to infer the possible prediction of SEP events. In our analysis, we use 126 SEP events with complete solar information, from 1997 to 2013. Each SEP event is a vector in six dimensions (corresponding to the six solar variables used in this work). The PCA transforms the input vectors into a set of orthogonal components. By mapping the characteristics of the parent solar events, a new base defined by these components led to the classification of the SEP events. We furthermore applied logistic regression analysis with single, as well as multiple explanatory variables, in order to develop a new index (\(I\)) for the nowcasting (short-term forecasting) of SEP events. We tested several different schemes for \(I\) and validated our findings with the implementation of categorical scores (probability of detection (POD) and false-alarm rate (FAR)). We present and interpret the obtained scores, and discuss the strengths and weaknesses of the different implementations. We show that \(I\) holds prognosis potential for SEP events. The maximum POD achieved is 77.78% and the relative FAR is 40.96%.


Solar energetic particle events Statistical methods Flares Coronal mass ejections Principal components analysis, logistic regression method 



AP would like to acknowledge support from a post-doctoral IKY scholarship funded by the action “Supporting post-doctoral researchers” from the resources of the b.p. “Human Resources Development Education and Lifelong Learning” with Priority Axes 6, 8, 9 and co-funded by the European Social Fund and the Greek government. AA would further like to acknowledge the “SPECS: Solar Particle Events and foreCasting Studies” research grant of the National Observatory of Athens. MP and RV acknowledge the funding from the Academy of Finland (decision 267186). Research conducted by MP and RV was further supported by ESA contract 4000120480/17/NL/LF/hh. The authors would further like to thank the anonymous referee for constructive comments that helped to improve the initial manuscript.

Disclosure of Potential Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS)National Observatory of AthensPenteliGreece
  2. 2.IRAPUniversité de Toulouse, CNRS, CNES, UPSToulouseFrance
  3. 3.Department of Physics and AstronomyUniversity of TurkuTurkuFinland
  4. 4.Institute of Terrestrial Magnetism, Ionosphere and Radiowave Propagation by N.V. Pushkov RAS (IZMIRAN)Moscow TroitskRussia

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