Advertisement

Solar Physics

, 293:100 | Cite as

Nowcasting Solar Energetic Particle Events Using Principal Component Analysis

  • A. Papaioannou
  • A. Anastasiadis
  • A. Kouloumvakos
  • M. Paassilta
  • R. Vainio
  • E. Valtonen
  • A. Belov
  • E. Eroshenko
  • M. Abunina
  • A. Abunin
Article

Abstract

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%.

Keywords

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

Notes

Acknowledgements

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.

References

  1. Abdi, H., Williams, L.J.: 2010, WIREs Comput. Stat. 2(4), 433. DOI. CrossRefGoogle Scholar
  2. Alberti, T., Laurenza, M., Cliver, E., Storini, M., Consolini, G., Lepreti, F.: 2017, Astrophys. J. 838(1), 59. DOI. ADSCrossRefGoogle Scholar
  3. Anastasiadis, A.: 2002, J. Atmos. Solar-Terr. Phys. 64(5), 481. DOI. ADSCrossRefGoogle Scholar
  4. Anastasiadis, A., Papaioannou, A., Sandberg, I., Georgoulis, M., Tziotziou, K., Kouloumvakos, A., Jiggens, P.: 2017, Solar Phys. 292(9), 134. DOI. ADSCrossRefGoogle Scholar
  5. Balch, C.C.: 1999, Radiat. Meas. 30(3), 231. DOI. CrossRefGoogle Scholar
  6. Balch, C.C.: 2008, Space Weather 6(1), S01001. DOI. ADSCrossRefGoogle Scholar
  7. Belov, A.: 2009, Adv. Space Res. 43(4), 467. DOI. ADSCrossRefGoogle Scholar
  8. Belov, A.: 2017, Geomagn. Aeron. 57(6), 727. DOI. ADSCrossRefGoogle Scholar
  9. Belov, A., Garcia, H., Kurt, V., Mavromichalaki, H., Gerontidou, M.: 2005, Solar Phys. 229(1), 135. DOI. ADSCrossRefGoogle Scholar
  10. Cane, H., Lario, D.: 2006, Space Sci. Rev. 123(1 – 3), 45. DOI. ADSCrossRefGoogle Scholar
  11. Cane, H., Richardson, I., Von Rosenvinge, T.: 2010, J. Geophys. Res. 115(A8), A08101. DOI. ADSCrossRefGoogle Scholar
  12. Chancellor, J.C., Scott, G.B., Sutton, J.P.: 2014, Life 4(3), 491. DOI. CrossRefGoogle Scholar
  13. Davis, J., Goadrich, M.: 2006, In: Proc. 23rd Inter. Conf. Machine Learning, 233. DOI. Google Scholar
  14. Dierckxsens, M., Tziotziou, K., Dalla, S., Patsou, I., Marsh, M., Crosby, N., Malandraki, O., Tsiropoula, G.: 2015, Solar Phys. 290(3), 841. DOI. ADSCrossRefGoogle Scholar
  15. Dresing, N., Gómez-Herrero, R., Klassen, A., Heber, B., Kartavykh, Y., Dröge, W.: 2012, Solar Phys. 281(1), 281. DOI. ADSGoogle Scholar
  16. Dröge, W., Kartavykh, Y., Klecker, B., Kovaltsov, G.: 2010, Astrophys. J. 709(2), 912. DOI. ADSCrossRefGoogle Scholar
  17. Engell, A., Falconer, D., Schuh, M., Loomis, J., Bissett, D.: 2017, Space Weather 15(10), 1321. DOI. ADSCrossRefGoogle Scholar
  18. Garcia, H.: 2004, Space Weather 2(6), S06003. DOI. ADSCrossRefGoogle Scholar
  19. Gómez-Herrero, R., Dresing, N., Klassen, A., Heber, B., Lario, D., Agueda, N., Malandraki, O., Blanco, J., Rodríguez-Pacheco, J., Banjac, S.: 2015, Astrophys. J. 799(1), 55. DOI. ADSCrossRefGoogle Scholar
  20. Govan, A.: 2006, North Carolina State University, SAMSI NDHS, Undergraduate workshop. https://projects.ncsu.edu/crsc/events/ugw06/presentations/aygovan/OptimizationUW06.pdf.
  21. Harrell, F.E.: 2001, Ordinal Logistic Regression, Springer, New York, 331. DOI. Google Scholar
  22. Head, J.D., Zerner, M.C.: 1985, Chem. Phys. Lett. 122(3), 264. DOI. ADSCrossRefGoogle Scholar
  23. Hosmer, D.W. Jr., Lemeshow, S., Sturdivant, R.X.: 2013, Applied Logistic Regression 398, John Wiley & Sons, Hoboken. CrossRefMATHGoogle Scholar
  24. Huang, X., Wang, H.-N., Li, L.-P.: 2012, Res. Astron. Astrophys. 12(3), 313. DOI. ADSCrossRefGoogle Scholar
  25. Iucci, N., Levitin, A., Belov, A., Eroshenko, E., Ptitsyna, N., Villoresi, G., Chizhenkov, G., Dorman, L., Gromova, L., Parisi, M., et al.: 2005, Space Weather 3(1), S01001. DOI. ADSCrossRefGoogle Scholar
  26. Jolliffe, I.: 2002, Principal Component Analysis, Springer, New York. DOI. MATHGoogle Scholar
  27. Kahler, S.: 2001, J. Geophys. Res. 106(A10), 20947. DOI. ADSCrossRefGoogle Scholar
  28. Kocharov, L., Torsti, J.: 2002, Solar Phys. 207(1), 149. DOI. ADSCrossRefGoogle Scholar
  29. Kouloumvakos, A., Patsourakos, S., Nindos, A., Vourlidas, A., Anastasiadis, A., Hillaris, A., Sandberg, I.: 2016, Astrophys. J. 821(1), 31. DOI. ADSCrossRefGoogle Scholar
  30. Kurt, V., Belov, A., Mavromichalaki, H., Gerontidou, M.: 2004, Ann. Geophys. 22(6), 2255. DOI. ADSCrossRefGoogle Scholar
  31. Lario, D., Kwon, R.-Y., Vourlidas, A., Raouafi, N., Haggerty, D., Ho, G., Anderson, B., Papaioannou, A., Gómez-Herrero, R., Dresing, N., et al.: 2016, Astrophys. J. 819(1), 72. DOI. ADSCrossRefGoogle Scholar
  32. Lario, D., Kwon, R.-Y., Richardson, I.G., Raouafi, N.E., Thompson, B., Von Rosenvinge, T.T., Mays, M.L., Mäkelä, P.A., Xie, H., Bain, H., et al.: 2017, Astrophys. J. 838(1), 51. DOI. ADSCrossRefGoogle Scholar
  33. Laurenza, M., Cliver, E., Hewitt, J., Storini, M., Ling, A., Balch, C., Kaiser, M.: 2009, Space Weather 7(4), S04008. DOI. ADSCrossRefGoogle Scholar
  34. Lim, M.: 2002, Occup. Environ. Med. 59(7), 428. DOI. CrossRefGoogle Scholar
  35. Mikaelian, T.: 2009, arXiv preprint. arXiv.
  36. Mishev, A.: 2014, Adv. Space Res. 54(3), 528. DOI. ADSCrossRefGoogle Scholar
  37. Miteva, R., Samwel, S.W., Krupar, V.: 2017, In: Georgieva, K., Kirov, B., Danov, D. (eds.) Proc. Ninth Workshop on Solar Influences on the Magnetosphere, Ionosphere and Atmosphere 30, 19. Google Scholar
  38. Núñez, M.: 2011, Space Weather 9(7), S07003. DOI. CrossRefGoogle Scholar
  39. Paassilta, M., Raukunen, O., Vainio, R., Valtonen, E., Papaioannou, A., Siipola, R., Riihonen, E., Dierckxsens, M., Crosby, N., Malandraki, O., et al.: 2017, J. Space Weather Space Clim. 7, A14. DOI. ADSCrossRefGoogle Scholar
  40. Papaioannou, A., Anastasiadis, A., Sandberg, I., Georgoulis, M., Tsiropoula, G., Tziotziou, K., Jiggens, P., Hilgers, A.: 2015, J. Phys. Conf. Ser. 632, 012075. DOI. CrossRefGoogle Scholar
  41. Papaioannou, A., Sandberg, I., Anastasiadis, A., Kouloumvakos, A., Georgoulis, M.K., Tziotziou, K., Tsiropoula, G., Jiggens, P., Hilgers, A.: 2016, J. Space Weather Space Clim. 6, A42. DOI. ADSCrossRefGoogle Scholar
  42. Park, J., Moon, Y.-J.: 2014, J. Geophys. Res. 119(12), 9456. DOI. CrossRefGoogle Scholar
  43. Park, J., Moon, Y.-J., Lee, H.: 2017, Astrophys. J. 844(1), 17. DOI. ADSCrossRefGoogle Scholar
  44. Park, J., Moon, Y.-J., Lee, D., Youn, S.: 2010, J. Geophys. Res. 115(A10), A10105. DOI. ADSGoogle Scholar
  45. Posner, A.: 2007, Space Weather 5(5), S05001. DOI. ADSCrossRefGoogle Scholar
  46. Reames, D.V.: 1999, Space Sci. Rev. 90(3 – 4), 413. DOI. ADSCrossRefGoogle Scholar
  47. Reames, D.V.: 2013, Space Sci. Rev. 175(1 – 4), 53. DOI. ADSCrossRefGoogle Scholar
  48. Reames, D.V.: 2017, Solar Energetic Particles, Lect. Notes Phys., Springer, Berlin. DOI. CrossRefGoogle Scholar
  49. Rouillard, A., Sheeley, N., Tylka, A., Vourlidas, A., Ng, C., Rakowski, C., Cohen, C., Mewaldt, R., Mason, G., Reames, D., et al.: 2012, Astrophys. J. 752(1), 44. DOI. ADSCrossRefGoogle Scholar
  50. Schraudolph, N.N., Yu, J., Günter, S.: 2007, In: Artificial Intelligence and Statistics, 436. Google Scholar
  51. Shevade, S.K., Keerthi, S.S.: 2003, Bioinformatics 19(17), 2246. DOI. CrossRefGoogle Scholar
  52. Shlens, J.: 2014, arXiv preprint. arXiv.
  53. Smart, D., Shea, M.: 1989, Adv. Space Res. 9(10), 281. DOI. ADSCrossRefGoogle Scholar
  54. Souvatzoglou, G., Papaioannou, A., Mavromichalaki, H., Dimitroulakos, J., Sarlanis, C.: 2014, Space Weather 12(11), 633. DOI. ADSCrossRefGoogle Scholar
  55. Tabachnick, B.G., Fidell, L.S.: 2007, Using Multivariate Statistics, Allyn & Bacon/Pearson Education, Needham Heights. Google Scholar
  56. Tobiska, W.K., Atwell, W., Beck, P., Benton, E., Copeland, K., Dyer, C., Gersey, B., Getley, I., Hands, A., Holland, M., et al.: 2015, Space Weather 13(4), 202. DOI. ADSCrossRefGoogle Scholar
  57. Trottet, G., Samwel, S., Klein, K.-L., de Wit, T.D., Miteva, R.: 2014, Solar Phys. 290, 819. DOI. ADSCrossRefGoogle Scholar
  58. Turner, R.E.: 2006, In: Gopalswamy, N., Mewaldt, R.A., Torsti, J. (eds.) Solar Eruptions and Energetic Particles, Geophys. Monograph Ser. 165, AGU Wiley Online Library, Hoboken, 367. DOI. Google Scholar
  59. Wiedenbeck, M., Mason, G., Cohen, C., Nitta, N., Gómez-Herrero, R., Haggerty, D.: 2012, Astrophys. J. 762(1), 54. DOI. ADSCrossRefGoogle Scholar
  60. Winter, L., Ledbetter, K.: 2015, Astrophys. J. 809(1), 105. DOI. ADSCrossRefGoogle Scholar

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

Personalised recommendations