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Nowcasting Solar Energetic Particle Events Using Principal Component Analysis

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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%.

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Notes

  1. The associated CMEs span from 1997 to 2013, with the availability of the continuous SOHO/LASCO measurements.

  2. The start time of an X-ray event is defined as the first minute, in a sequence of four minutes, of steep monotonic increase in the 0.1 – 0.8 nm flux. The end time is the time when the flux level decays to a point halfway between the maximum flux and the pre-flare background level. This means that the duration time (\(\mathit{DT}\)) is the time difference between the end and the start time of the flare.

  3. In the weighted PCA, scores are calculated as follows: \(X-\mathit{mean}(X)/\mathit{variance}(X)\).

  4. http://www.swpc.noaa.gov/noaa-scales-explanation .

References

  • Abdi, H., Williams, L.J.: 2010, WIREs Comput. Stat. 2(4), 433. DOI .

    Google Scholar 

  • Alberti, T., Laurenza, M., Cliver, E., Storini, M., Consolini, G., Lepreti, F.: 2017, Astrophys. J. 838(1), 59. DOI .

    ADS  Google Scholar 

  • Anastasiadis, A.: 2002, J. Atmos. Solar-Terr. Phys. 64(5), 481. DOI .

    ADS  Google Scholar 

  • Anastasiadis, A., Papaioannou, A., Sandberg, I., Georgoulis, M., Tziotziou, K., Kouloumvakos, A., Jiggens, P.: 2017, Solar Phys. 292(9), 134. DOI .

    ADS  Google Scholar 

  • Balch, C.C.: 1999, Radiat. Meas. 30(3), 231. DOI .

    Google Scholar 

  • Balch, C.C.: 2008, Space Weather 6(1), S01001. DOI .

    ADS  Google Scholar 

  • Belov, A.: 2009, Adv. Space Res. 43(4), 467. DOI .

    ADS  Google Scholar 

  • Belov, A.: 2017, Geomagn. Aeron. 57(6), 727. DOI .

    ADS  Google Scholar 

  • Belov, A., Garcia, H., Kurt, V., Mavromichalaki, H., Gerontidou, M.: 2005, Solar Phys. 229(1), 135. DOI .

    ADS  Google Scholar 

  • Cane, H., Lario, D.: 2006, Space Sci. Rev. 123(1 – 3), 45. DOI .

    ADS  Google Scholar 

  • Cane, H., Richardson, I., Von Rosenvinge, T.: 2010, J. Geophys. Res. 115(A8), A08101. DOI .

    ADS  Google Scholar 

  • Chancellor, J.C., Scott, G.B., Sutton, J.P.: 2014, Life 4(3), 491. DOI .

    Google Scholar 

  • Davis, J., Goadrich, M.: 2006, In: Proc. 23rd Inter. Conf. Machine Learning, 233. DOI .

    Google Scholar 

  • Dierckxsens, M., Tziotziou, K., Dalla, S., Patsou, I., Marsh, M., Crosby, N., Malandraki, O., Tsiropoula, G.: 2015, Solar Phys. 290(3), 841. DOI .

    ADS  Google Scholar 

  • Dresing, N., Gómez-Herrero, R., Klassen, A., Heber, B., Kartavykh, Y., Dröge, W.: 2012, Solar Phys. 281(1), 281. DOI .

    ADS  Google Scholar 

  • Dröge, W., Kartavykh, Y., Klecker, B., Kovaltsov, G.: 2010, Astrophys. J. 709(2), 912. DOI .

    ADS  Google Scholar 

  • Engell, A., Falconer, D., Schuh, M., Loomis, J., Bissett, D.: 2017, Space Weather 15(10), 1321. DOI .

    ADS  Google Scholar 

  • Garcia, H.: 2004, Space Weather 2(6), S06003. DOI .

    ADS  Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Govan, A.: 2006, North Carolina State University, SAMSI NDHS, Undergraduate workshop. https://projects.ncsu.edu/crsc/events/ugw06/presentations/aygovan/OptimizationUW06.pdf .

  • Harrell, F.E.: 2001, Ordinal Logistic Regression, Springer, New York, 331. DOI .

    Google Scholar 

  • Head, J.D., Zerner, M.C.: 1985, Chem. Phys. Lett. 122(3), 264. DOI .

    ADS  Google Scholar 

  • Hosmer, D.W. Jr., Lemeshow, S., Sturdivant, R.X.: 2013, Applied Logistic Regression 398, John Wiley & Sons, Hoboken.

    Book  MATH  Google Scholar 

  • Huang, X., Wang, H.-N., Li, L.-P.: 2012, Res. Astron. Astrophys. 12(3), 313. DOI .

    ADS  Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Jolliffe, I.: 2002, Principal Component Analysis, Springer, New York. DOI .

    MATH  Google Scholar 

  • Kahler, S.: 2001, J. Geophys. Res. 106(A10), 20947. DOI .

    ADS  Google Scholar 

  • Kocharov, L., Torsti, J.: 2002, Solar Phys. 207(1), 149. DOI .

    ADS  Google Scholar 

  • Kouloumvakos, A., Patsourakos, S., Nindos, A., Vourlidas, A., Anastasiadis, A., Hillaris, A., Sandberg, I.: 2016, Astrophys. J. 821(1), 31. DOI .

    ADS  Google Scholar 

  • Kurt, V., Belov, A., Mavromichalaki, H., Gerontidou, M.: 2004, Ann. Geophys. 22(6), 2255. DOI .

    ADS  Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Laurenza, M., Cliver, E., Hewitt, J., Storini, M., Ling, A., Balch, C., Kaiser, M.: 2009, Space Weather 7(4), S04008. DOI .

    ADS  Google Scholar 

  • Lim, M.: 2002, Occup. Environ. Med. 59(7), 428. DOI .

    Google Scholar 

  • Mikaelian, T.: 2009, arXiv preprint. arXiv .

  • Mishev, A.: 2014, Adv. Space Res. 54(3), 528. DOI .

    ADS  Google Scholar 

  • 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 

  • Núñez, M.: 2011, Space Weather 9(7), S07003. DOI .

    Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Papaioannou, A., Anastasiadis, A., Sandberg, I., Georgoulis, M., Tsiropoula, G., Tziotziou, K., Jiggens, P., Hilgers, A.: 2015, J. Phys. Conf. Ser. 632, 012075. DOI .

    Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Park, J., Moon, Y.-J.: 2014, J. Geophys. Res. 119(12), 9456. DOI .

    Google Scholar 

  • Park, J., Moon, Y.-J., Lee, H.: 2017, Astrophys. J. 844(1), 17. DOI .

    ADS  Google Scholar 

  • Park, J., Moon, Y.-J., Lee, D., Youn, S.: 2010, J. Geophys. Res. 115(A10), A10105. DOI .

    ADS  Google Scholar 

  • Posner, A.: 2007, Space Weather 5(5), S05001. DOI .

    ADS  Google Scholar 

  • Reames, D.V.: 1999, Space Sci. Rev. 90(3 – 4), 413. DOI .

    ADS  Google Scholar 

  • Reames, D.V.: 2013, Space Sci. Rev. 175(1 – 4), 53. DOI .

    ADS  Google Scholar 

  • Reames, D.V.: 2017, Solar Energetic Particles, Lect. Notes Phys., Springer, Berlin. DOI .

    Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Schraudolph, N.N., Yu, J., Günter, S.: 2007, In: Artificial Intelligence and Statistics, 436.

    Google Scholar 

  • Shevade, S.K., Keerthi, S.S.: 2003, Bioinformatics 19(17), 2246. DOI .

    Google Scholar 

  • Shlens, J.: 2014, arXiv preprint. arXiv .

  • Smart, D., Shea, M.: 1989, Adv. Space Res. 9(10), 281. DOI .

    ADS  Google Scholar 

  • Souvatzoglou, G., Papaioannou, A., Mavromichalaki, H., Dimitroulakos, J., Sarlanis, C.: 2014, Space Weather 12(11), 633. DOI .

    ADS  Google Scholar 

  • Tabachnick, B.G., Fidell, L.S.: 2007, Using Multivariate Statistics, Allyn & Bacon/Pearson Education, Needham Heights.

    Google Scholar 

  • 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 .

    ADS  Google Scholar 

  • Trottet, G., Samwel, S., Klein, K.-L., de Wit, T.D., Miteva, R.: 2014, Solar Phys. 290, 819. DOI .

    ADS  Google Scholar 

  • 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 

  • Wiedenbeck, M., Mason, G., Cohen, C., Nitta, N., Gómez-Herrero, R., Haggerty, D.: 2012, Astrophys. J. 762(1), 54. DOI .

    ADS  Google Scholar 

  • Winter, L., Ledbetter, K.: 2015, Astrophys. J. 809(1), 105. DOI .

    ADS  Google Scholar 

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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.

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Appendix

Appendix

Table 4 The 126 SEP events employed in the PCA. Column 1 provides the date of the related solar flare in the form year.month.day, column 2 shows the peak time of the SF, columns 3 to 8 provide the six variables used in our analysis, namely: CME width (\(s\)), and velocity (\(u\)), logarithm of the solar flare (SF) magnitude (\(\log\mathit{SXRs}\)), SF longitude (\(\mathit{lon}\)), duration (\(\mathit{DT}\)), and rise time (\(\mathit{RT}\)). Columns 9 and 10 give the SEP nowcasting results (where Hit and Miss refers to SEPs correctly predicted and SEPs that were not predicted) for \(I^{(3)}\) and \(I^{(3+O^{2})}\), respectively.

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Papaioannou, A., Anastasiadis, A., Kouloumvakos, A. et al. Nowcasting Solar Energetic Particle Events Using Principal Component Analysis. Sol Phys 293, 100 (2018). https://doi.org/10.1007/s11207-018-1320-7

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