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Solar System Research

, Volume 45, Issue 6, pp 546–556 | Cite as

Hierarchical approach to forecasting recurrent solar wind streams

  • Yu. S. Shugay
  • I. S. Veselovsky
  • D. B. Seaton
  • D. Berghmans
Article

Abstract

The hierarchical approach to predicting quasi-stationary, high-speed solar wind (SW) streams is described. This approach integrates various types of data into a single forecasting system by means of an ensemble of experts. The input data included the daily values of the coronal hole areas, which were calculated from the ultraviolet images of the Sun, and the speed of the SW streams during the previous solar rotations. The coronal hole areas were calculated from the images taken by the SWAP instrument aboard the PROBA2 satellite in the spectral interval centered at a wavelength of 17.4 nm and by the AIA instrument aboard the SDO spacecraft in the interval of wavelengths centered at 19.3 and 17.1 nm. The forecast was based on the data for 2010, corresponding to the rising phase of the 24th solar cycle. On the first hierarchical level, a few simple model estimates were obtained for the speed of the SW streams from the input data of each type. On the second level of hierarchy, the final 3 day ahead forecast of the SW velocity was formulated on the basis of the obtained estimates. The proposed hierarchical approach improves the accuracy of forecasting the SW velocity. In addition, in such a method of prediction, the data gaps in the records of one instrument do not crucially affect the final result of forecasting of the system as a whole.

Keywords

Solar Wind Root Mean Square Error Solar Cycle Coronal Hole Solar System Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Abramenko V., Yurchyshyn V., and Watanabe, H., Parameters of the Magnetic Flux inside Coronal Hole, Sol. Phys., 2009, vol. 250, no.1, pp. 43–57.ADSCrossRefGoogle Scholar
  2. Arge, C.N. and Pizzo, V.J., Improvement in the Prediction of Solar Wind Condition using Near-Real Time Solar Magnetic Field Update, J. Geophys. Res., 2000, vol. 105, no. A5, pp. 10465–10479.ADSCrossRefGoogle Scholar
  3. Aschwanden, M.J., Image Processing Techniques and Feature Recognition in Solar Physics, Sol. Phys., 2010, vol. 262, pp. 235–275.ADSCrossRefGoogle Scholar
  4. Barra, V., Delouille, V., Kretzschmar, M., and Hochedez, J., Fast and Robust Segmentation of Solar EUV Images: Algorithm and Results for Solar Cycle 23, Astron. Astrophys., 2009, vol. 505, pp. 361–371.ADSCrossRefGoogle Scholar
  5. Berghmans, D., Hochedez, J.-F., Defise, J.M., et al., SWAP onboard PROBA2, a New EUV Imager for Solar Monitoring, Adv. Space Res., 2006, vol. 38, pp. 1807–1811.ADSCrossRefGoogle Scholar
  6. Brown, G., Ensemble Learning, in Encyclopedia of Machine Learning, Sammut, C. and Weeb, G.I., Eds., Springer Press, 2010, pp. 312–320.Google Scholar
  7. Chapman, S. and Bartels, J., Geomagnetism, Oxford: Clarendon, 1940.Google Scholar
  8. Cranmer, S., Coronal Holes, Living Rev. Solar Phys., 2009, vol. 6, no. 3, pp. 1–65.ADSGoogle Scholar
  9. De Toma, G., Evolution of Coronal Holes and Implications for High-Speed Solar Wind during the Minimum between Cycles 23 and 24, Sol. Phys., 2011, in press.Google Scholar
  10. Del Zanna, G., Andretta, V., Poletto, G., et al., Multi-Instrument Campaigning to Observe the Off-Limb Corona, in Proc. Second Hinode Sci. Meet., ASP Conf. Ser., Boulder, Colorado USA, 2009, vol. 415, pp. 315–318.ADSGoogle Scholar
  11. Dolenko, S.A., Orlov, Yu.V., Persiantsev, I.G., and Shugay, Yu.S., Neural Network Algorithms for Analyzing Multidimensional Time Series for Predicting Events and Their Application to Study of Sun-Earth Relations, Patt. Recogn. Image Analys., 2007, vol. 17, no. 4, pp. 584–591.CrossRefGoogle Scholar
  12. Eselevich, V.G., Fainshtein, V.G., Rudenko, G.V., et al., Forecasting the Velocity of Quasi-Stationary Solar Wind and the Intensity of Geomagnetic Disturbances Produced by It, Cosm. Res., 2009, vol. 47, no. 2, pp. 95–113.ADSCrossRefGoogle Scholar
  13. Halain, J.-P., Berghmans, D., Defise, J.-M., et al., First Light of SWAP on-board PROBA2, Proc. SPIE, 2010, vol. 7732, pp. 77320P–77320P-11.CrossRefGoogle Scholar
  14. Hansen, J.V. and Nelson, R.D., Data Mining of Time Series Using Stacked Generalizers, Neurocomputing, 2002, vol. 43, pp. 173–184.zbMATHCrossRefGoogle Scholar
  15. Harvey, J.W. and Sheeley, N.R., Coronal Holes, Solar Wind Streams, and Geomagnetic Activity during the New Sunspot Cycle, Sol. Phys., 1978, vol. 59, pp. 159–173.ADSCrossRefGoogle Scholar
  16. Hurlburt, N., Cheung, M., Schrijver, C., et al., Heliophysics Event Knowledgebase for the Solar Dynamics Observatory (SDO) and beyond, Sol. Phys., 2010. doi: 10.1007/s11207-010-9624-2Google Scholar
  17. Jacobs, R.A., Jordan, M.I., and Barto, A.G., Task Decomposition through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks, Cognitive Sci., 1991, vol. 15, pp. 219–250.CrossRefGoogle Scholar
  18. Krista, L. and Gallagher, P., Automatic Coronal Hole Detection Using Local Intensity Thresholding Techniques, Sol. Phys., 2009, vol. 256, pp. 87–100.ADSCrossRefGoogle Scholar
  19. Luo, B., Zhong, Q., Lui, S., and Gong, J., A New Forecasting Index for Solar Wind Velocity Based on EIT 284 Å Observations, Sol. Phys., 2008, vol. 250, pp. 159–170.ADSCrossRefGoogle Scholar
  20. Madjarska, M.S. and Wiegelmann, T., Coronal Hole Boundaries Evolution at Small Scales: I EIT 19.5 nm and TRACE 17.1 nm View, Astron. Astrophys., 2009, vol. 503, pp. 991–997.ADSCrossRefGoogle Scholar
  21. Nolte, J.T., Krieger, A.S., Timothy, A.F., et al., Coronal Holes as Source of Solar Wind, Sol. Phys., 1976, vol. 46, pp. 303–322.ADSCrossRefGoogle Scholar
  22. Obridko, V.N., Shelting, B.D., Livshits, I.M., and Asgarov, A.B., Contrast of Coronal Holes and Parameters of Associated Solar Wind Streams, Sol. Phys., 2009, vol. 260, no. 1, pp. 191–206.ADSCrossRefGoogle Scholar
  23. Persiantsev, I.G., Ryazanov, A.Yu., and Shugay, Ju.S., The Automatic Processing and Analysis of Solar Image Sequences, Patt. Recogn. Image Analys., 2006, vol. 16, no. 1, pp. 30–32.CrossRefGoogle Scholar
  24. Robbins, S.J., Henney, C.J., and Harvey, J.W., Solar Wind Forecasting with Coronal Holes, Sol. Phys., 2006, vol. 233, pp. 265–276.ADSCrossRefGoogle Scholar
  25. Scholl, F.I. and Habbal, S.R., Automatic Detection and Classification of Coronal Holes and Filaments Based on EUV and Magnetogram Observation of Solar Disc, Sol. Phys., 2008, vol. 248, pp. 425–439.ADSCrossRefGoogle Scholar
  26. Sheeley, N.R., Harvey, J.W., and Feldman, W.C., Coronal Holes, Solar Wind Streams and Recurrent Geomagnetic Disturbances 1973–1976, Sol. Phys., 1976, vol. 49, pp. 271–278.ADSCrossRefGoogle Scholar
  27. Shnirman, M., Le Mouél, J.-L., and Blanter, E., Slow and Fast Rotating Coronal Holes from Geomagnetic Indices, Sol. Phys., 2010, vol. 266, no. 1, pp. 159–171.ADSCrossRefGoogle Scholar
  28. Shugay, J.S., Guzhva, A.G., Dolenko, S.A., and Persiantsev, I.G., An Algorithm for Construction of a Hierarchical Neural Network Complex for Time Series Analysis and its Application for Studying Sun-Earth Relations, in Proc. 8th Int. Conf. Patt. Recognit. Image Analys.: New Information Technologies (PRIA-8-2007), Yoshkar-Ola, 2007, vol. 2, pp. 335–358.Google Scholar
  29. Shugay, Yu.S., Veselovsky, I.S., and Trichtchenko, L.D., Studying Correlations between the Coronal Hole Area, Solar Wind Velocity, and Local Magnetic Indices in the Canadian Region during the Decline Phase of Cycle 23, Geomagn. Aeron., 2009, vol. 49, no. 4, pp. 415–424.ADSCrossRefGoogle Scholar
  30. Shugay, Yu.S. and Veselovsky, I.S., Studying the Structure of Coronal Holes in Different Wavelength Ranges, in Tr. Konf. “Mnogovolnovye Issledovaniya Solntsa i Sovremennye Problemy Solnechnoi Aktivnosti” (Proc. Conf. on Multiwave Studies of the Sun and Modern Problems in Solar Activity), Nizhni Arkhyz, 2007, pp. 92–100.Google Scholar
  31. Stepanian, N.N., Kuzin, S.V., and Fainshtein, V.G., Relationship between the Coronal Holes and High-Speed Streams of Solar Wind, Sol. Syst. Res., 2008, vol. 42, no. 1, pp. 83–89.ADSCrossRefGoogle Scholar
  32. Terekhov, S.A., Brilliant Committees of Clever Machines, in Lektsii po neiroinformatike 10-i Vserossiiskoi nauchno-tekhnicheskoi konferentsii “Neiroinformatika-2008”. Sb. nauch. tr. (Lectures on Neuroinformatics, Proc. 10th All-Rus. Sci.-Tech. Conf. “Neuroinfiormatics-2008”), Moscow, 2008, part 2, pp. 11–42.Google Scholar
  33. Veselovsky, I.S., Persiantsev, I.G., Ryazanov, A.Yu., and Shugay, Yu.S., One-Parameter Representation of the Daily Averaged Solar-Wind Velocity, Sol. Syst. Res., 2006, vol. 40, no. 5, pp. 427–431.ADSCrossRefGoogle Scholar
  34. Veselovsky, I.S., Persiantsev, I.G., and Shugay, Yu.S., Forecast of the Solar Wind Velocity and the Interplanetary Magnetic Field Radial Component Polarity at the Phase of Decay of Solar Cycle 23, Geomagn. Aeron., 2006, vol. 46, no. 6, pp. 701–707.ADSCrossRefGoogle Scholar
  35. Vrsnak, B., Temmer, M., and Veronig, A.M., Coronal Holes and Solar Wind High-Speed Streams: I. Forecasting the Solar Wind Parameters, Sol. Phys., 2007, vol. 240, no. 2, pp. 315–330.ADSCrossRefGoogle Scholar
  36. Wang, Y.-M. and Sheeley, N.R., Jr., Solar Wind Speed and Coronal Flux-Tube Expansion, Astrophys. J., 1990, vol. 355, pp. 726–732.ADSCrossRefGoogle Scholar
  37. Wintoft, P. and Lundstedt, H., Neural Network Study of Mapping from Solar Magnetic Fields to the Daily Average Solar Wind Velocity, J. Geophys. Res., 1999, vol. 104, no. A4, pp. 6729–6736.ADSCrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2011

Authors and Affiliations

  • Yu. S. Shugay
    • 1
  • I. S. Veselovsky
    • 1
    • 2
  • D. B. Seaton
    • 3
  • D. Berghmans
    • 3
  1. 1.Skobeltsyn Institute of Nuclear PhysicsMoscow State UniversityMoscowRussia
  2. 2.Space Research InstituteRussian Academy of SciencesMoscowRussia
  3. 3.Royal Observatory of BelgiumBrusselsBelgium

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