Predicting Flares and Solar Energetic Particle Events: The FORSPEF Tool

Abstract

A novel integrated prediction system for solar flares (SFs) and solar energetic particle (SEP) events is presented here. The tool called forecasting solar particle events and flares (FORSPEF) provides forecasts of solar eruptive events, such as SFs with a projection to occurrence and velocity of coronal mass ejections (CMEs), and the likelihood of occurrence of an SEP event. In addition, the tool provides nowcasting of SEP events based on actual SF and CME near real-time data, as well as the SEP characteristics (e.g. peak flux, fluence, rise time, and duration) per parent solar event. The prediction of SFs relies on the effective connected magnetic field strength (\(B_{\mathrm{eff}}\)) metric, which is based on an assessment of potentially flaring active-region (AR) magnetic configurations, and it uses a sophisticated statistical analysis of a large number of AR magnetograms. For the prediction of SEP events, new statistical methods have been developed for the likelihood of the SEP occurrence and the expected SEP characteristics. The prediction window in the forecasting scheme is 24 hours with a refresh rate of 3 hours, while the respective prediction time for the nowcasting scheme depends on the availability of the near real-time data and ranges between 15 – 20 minutes for solar flares and 6 hours for CMEs. We present the modules of the FORSPEF system, their interconnection, and the operational setup. Finally, we demonstrate the validation of the modules of the FORSPEF tool using categorical scores constructed on archived data, and we also discuss independent case studies.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Notes

  1. 1.

    Magnetic flux imbalance is defined as the ratio \(\frac{\sum_{i} B_{zi}}{\sum_{i} \mid B_{zi} \mid}\). The summation refers to the entire AR area as identified by the ARIA.

  2. 2.

    Type III bursts signify the propagation of beams of nonthermal electrons in the solar atmosphere and the solar system. As a result, they provide information on electron escape, acceleration, and transport, as well as on the conditions of the background ambient plasma they travel through.

References

  1. Abramenko, V.I., Yurchyshyn, V.B., Wang, H., Spirock, T.J., Goode, P.R.: 2002, Astrophys. J. 577, 487. DOI .

    ADS  Article  Google Scholar 

  2. Abramenko, V.I., Yurchyshyn, V.B., Wang, H., Spirock, T.J., Goode, P.R.: 2003, Astrophys. J. 597, 1135. DOI .

    ADS  Article  Google Scholar 

  3. Alberti, T., Laurenza, M., Cliver, E.W., Storini, M., Consolini, G., Lepreti, F.: 2017, Astrophys. J. 838(1), 59. http://stacks.iop.org/0004-637X/838/i=1/a=59 .

    ADS  Article  Google Scholar 

  4. Anastasiadis, A.: 2002, J. Atmos. Solar-Terr. Phys. 64, 481. DOI .

    ADS  Article  Google Scholar 

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

    Article  Google Scholar 

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

    ADS  Article  Google Scholar 

  7. Bütikofer, R., Flückiger, E., Desorgher, L., Moser, M.: 2008, Sci. Total Environ. 391(2), 177.

    ADS  Article  Google Scholar 

  8. Cane, H.V., Lario, D.: 2006, Space Sci. Rev. 123, 45. DOI .

    ADS  Article  Google Scholar 

  9. Dimitropoulou, M., Georgoulis, M., Isliker, H., Vlahos, L., Anastasiadis, A., Strintzi, D., Moussas, X.: 2009, Astron. Astrophys. 505, 1245. DOI .

    ADS  Article  Google Scholar 

  10. Falconer, D., Moore, R., Gary, A.: 2007, In: American Astronomical Society Meeting Abstracts #210, Bulletin of the American Astronomical Society 39, 135.

    Google Scholar 

  11. Georgoulis, M.K.: 2005, Solar Phys. 228, 5. DOI .

    ADS  Article  Google Scholar 

  12. Georgoulis, M.K.: 2008, Geophys. Res. Lett. 35, L06S02. DOI .

    Article  Google Scholar 

  13. Georgoulis, M.K.: 2012, Astrophys. Space Sci. Proc. 30, 93. DOI .

    ADS  Article  Google Scholar 

  14. Georgoulis, M.K., Rust, D.M.: 2007, Astrophys. J. Lett. 661, 109. DOI .

    ADS  Article  Google Scholar 

  15. Georgoulis, M., Raouafi, N.-E., Henney, C.: 2008, ASP Conf. Ser. 383, 107.

    ADS  Google Scholar 

  16. Hapgood, M., Thomson, A.: 2010, Space weather: Its impact on earth and implications for business, Lloyd’s 360 Risk Insight.

  17. Holmes-Siedle, A., Adams, L.: 1993, Handbook of radiation effects.

  18. James, T., Subramanian, P., Kontar, E.P.: 2017, Mon. Not. Roy. Astron. Soc. 471(1), 89. DOI .

    ADS  Article  Google Scholar 

  19. Jiggens, P., Chavy-Macdonald, M.-A., Santin, G., Menicucci, A., Evans, H., Hilgers, A.: 2014, J. Space Weather Space Clim. 4, A20.

    Article  Google Scholar 

  20. Kraaikamp, E., Verbeeck, C.: 2015, J. Space Weather Space Clim. 5, A18.

    ADS  Article  Google Scholar 

  21. Krucker, S., White, S.M., Lin, R.P.: 2007, Astrophys. J. Lett. 669(1), L49. http://stacks.iop.org/1538-4357/ 669/i=1/a=L49 .

    ADS  Article  Google Scholar 

  22. LaBonte, B., Georgoulis, M., Rust, D.: 2007, Astrophys. J. 671(1), 955.

    ADS  Article  Google Scholar 

  23. Laurenza, M., Cliver, E.W., Hewitt, J., Storini, M., Ling, A.G., Balch, C.C., Kaiser, M.L.: 2009, Space Weather 7, S04008. DOI .

    ADS  Article  Google Scholar 

  24. Leka, K.D., Barnes, G.: 2007, Astrophys. J. 656, 1173. DOI .

    ADS  Article  Google Scholar 

  25. Mason, J.P., Hoeksema, J.T.: 2010, Astrophys. J. 723, 634. DOI .

    ADS  Article  Google Scholar 

  26. McAteer, R.T.J., Gallagher, P.T., Ireland, J.: 2005, Astrophys. J. 631, 628. DOI .

    ADS  Article  Google Scholar 

  27. Moon, Y.-J., Choe, G.S., Yun, H.S., Park, Y.D.: 2001, J. Geophys. Res. 106, 29951. DOI .

    ADS  Article  Google Scholar 

  28. Núñez, M.: 2011, Space Weather 9(7).

  29. Papaioannou, A., Anastasiadis, A., Sandberg, I., Georgoulis, M.K., Tsiropoula, G., Tziotziou, K., Jiggens, P., Hilgers, A.: 2015, J. Phys. Conf. Ser. 632(1), 012075. http://stacks.iop.org/1742-6596/632/i=1/ a=012075 .

    Article  Google Scholar 

  30. 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(27), A42. DOI .

    ADS  Article  Google Scholar 

  31. Papaioannou, A., Anastasiadis, A., Sandberg, I., Jiggens, P.: 2017, J. Space Weather Space Clim.

  32. Patsourakos, S., Georgoulis, M., Vourlidas, A., Nindos, A., Sarris, T., Anagnostopoulos, G., Anastasiadis, A., Chintzoglou, G., Daglis, I., Gontikakis, C., et al.: 2016, Astrophys. J. 817(1), 14.

    ADS  Article  Google Scholar 

  33. Qahwaji, R., Colak, T.: 2007, Solar Phys. 241, 195. DOI .

    ADS  Article  Google Scholar 

  34. Robbrecht, E., Berghmans, D.: 2004, Astron. Astrophys. 425(3), 1097.

    ADS  Article  Google Scholar 

  35. Robbrecht, E., Berghmans, D., Van der Linden, R.: 2009, Astrophys. J. 691(2), 1222.

    ADS  Article  Google Scholar 

  36. Sandberg, I., Jiggens, P., Heynderickx, D., Daglis, I.A.: 2014, Geophys. Res. Lett. 41, 4435. DOI .

    ADS  Article  Google Scholar 

  37. Schrijver, C.J.: 2007, Astrophys. J. Lett. 655, 117. DOI .

    ADS  Article  Google Scholar 

  38. Smart, D.F., Shea, M.A.: 1989, J. Spacecr. Rockets 26, 403. DOI .

    ADS  Article  Google Scholar 

  39. Srour, J.R., McGarrity, J.M.: 1988, Proc. IEEE 76(11), 1443.

    ADS  Article  Google Scholar 

  40. Uritsky, V.M., Paczuski, M., Davila, J.M., Jones, S.I.: 2007, Phys. Rev. Lett. 99(2), 025001. DOI .

    ADS  Article  Google Scholar 

  41. Uritsky, V.M., Davila, J.M., Ofman, L., Coyner, A.J.: 2013, Astrophys. J. 769, 62. DOI .

    ADS  Article  Google Scholar 

  42. Vainio, R., Valtonen, E., Heber, B., Malandraki, O.E., Papaioannou, A., Klein, K.-L., Afanasiev, A., Agueda, N., Aurass, H., Battarbee, M., Braune, S., Dröge, W., Ganse, U., Hamadache, C., Heynderickx, D., Huttunen-Heikinmaa, K., Kiener, J., Kilian, P., Kopp, A., Kouloumvakos, A., Maisala, S., Mishev, A., Miteva, R., Nindos, A., Oittinen, T., Raukunen, O., Riihonen, E., Rodríguez-Gasén, R., Saloniemi, O., Sanahuja, B., Scherer, R., Spanier, F., Tatischeff, V., Tziotziou, K., Usoskin, I.G., Vilmer, N.: 2013, J. Space Weather Space Clim. 3(27), A12. DOI .

    Article  Google Scholar 

  43. Yashiro, S., Gopalswamy, N., Akiyama, S., Michalek, G., Howard, R.A.: 2005, J. Geophys. Res. 110, A12S05. DOI .

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported through the ESA Contract No. 4000109641/13/NL/AK “Improvement of Solar Particle Events and Flare Prediction”. AA acknowledges the support through “SPECS: Solar Particle Events and foreCasting Studies” research grant of the National Observatory of Athens. The authors would like to thank Karl-Ludwig Klein and Angels Aran for constructive discussions. This article uses data from the CACTus CME catalog and the Solar Demon tool, generated and maintained by the Solar Influences Data Analysis Center (SIDC) at the Royal Observatory of Belgium (ROB). The provision of the SDO/HMI data by the Stanford Solar Group is also gratefully acknowledged.

Author information

Affiliations

Authors

Corresponding author

Correspondence to A. Anastasiadis.

Ethics declarations

Disclosure of Potential Conflict of Interest

The authors declare that they have no conflicts of interest.

Additional information

Combined Radio and Space-based Solar Observations: From Techniques to New Results

Guest Editors: Eduard Kontar and Alexander Nindos

Appendix A: Categorical Scores

Appendix A: Categorical Scores

In general, in order to derive categorical scores, we need to enable the construction of contingency tables and to calculate the various skill scores stemming from them. We imposed thresholds on predictive probabilities above/below which a YES/NO prediction was adopted, and we inferred the skill scores as a function of these thresholds. However, the application of categorical measures to probabilistic forecasts requires the definition of a probabilistic threshold, \(\mathrm{pt}\). If the forecast/nowcast probability is \(\ge\mathrm{pt}\), a warning is issued, while if the forecast/nowcast probability is \(< \mathrm{pt}\), no warning is issued. Based on this, it is possible to construct a contingency table (see Table 2) and calculate event-based norms.

Table 2 Classical \(2 \times2\) contingency table for dichotomous forecasting on a total of N predictions. Table elements correspond to (a) hits, corresponding to events that were predicted and observed, (b) false positives or false alarms, corresponding to events that were predicted but not observed, (c) misses, corresponding to events that were not predicted but observed, and (d) true negatives, corresponding to events that were neither predicted nor observed.

For our purposes of validation, we used the following scores:

  • The overall accuracy (OA) with a perfect score of 1 achieved in the case that there are no misses and no false positives: \(\mathrm{OA}= a+d / N \).

  • The probability of detection (POD), which ignores false positives and true negatives and achieves a perfect score of 1 in the case that there are no misses: \(\mathrm{POD}= a / a+c\).

  • The critical success index (CSI), which ignores true negatives and achieves a perfect score of 1 in the case that there are no false positives and no misses: \(\mathrm{CSI}= a / a+b+c\).

  • The probability of a false alarm (PFA) or false-alarm rate (FAR), which ignores misses and true negatives and achieves a perfect score of 0 in the case that there are no false positives: \(\mathrm{FAR}=\mathrm{PFA}= b / a+b\).

  • The Heidke skill score (HSS), which was used to quantify the ability of achieving correct predictions with respect to chance. A value of 0 or lower implies that correct predictions could be completely due to chance, while a perfect score of 1 is achieved in the opposite case: \(\mathrm{HSS}= 2(ad-bc)/(a+c)(c+d)+(a+b)(b+d)\).

  • The percent correct (PC), which provides the ratio of correct predictions as a percentage of the total number of forecasts: \(\mathrm{PC}= a+c / N\).

  • The true skill statistic (TSS) obtains values in the range \([-1,1]\) with a perfect score of 1 attained in the case that there are no false positives and no misses (same as CSI), and a totally unskilled value of −1 attained in the case that there are no hits and no true negatives. A value of 0 demonstrates equal “sensitivity” to hits, compared to false positives, and misses, compares to true negatives: \(\mathrm{TSS}= a / (a+b)- c /(c+d)\).

For probabilistic forecasts, we can treat the probability threshold, \(\mathrm{pt}\), as an independent variable ranging within \([0.0, 1.0]\) and calculate the categorical scores (POD, FAR, HSS, and PC) per \(\mathrm{pt}\) value. It is expected to identify a decrease of FAR while \(\mathrm{pt}\) increases. At the same time, however, a decrease in POD is also marked. The optimal score is traced using HSS for a value of \(\mathrm{pt}\).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Anastasiadis, A., Papaioannou, A., Sandberg, I. et al. Predicting Flares and Solar Energetic Particle Events: The FORSPEF Tool. Sol Phys 292, 134 (2017). https://doi.org/10.1007/s11207-017-1163-7

Download citation

Keywords

  • Sun: coronal mass ejections (CMEs)
  • Sun: corona
  • Sun: radio radiation
  • Sun: solar energetic particle events
  • Sun: forecasting systems
  • Sun: integrated tools