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Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

Abstract

An operational model was developed for forecasting ionospheric variations and storms at Kokubunji (35N, 139E), 24 hours in advance, by using a neural network. The ionospheric critical frequency (foF2) shows periodic variabilities from days to the solar cycle length and also shows sporadic changes known as ionospheric storms caused by geomagnetic storms (of solar disturbance origin). The neural network was trained for the target parameter of foF2 at each local time and input parameters of solar flux, sunspot number, day of the year, Kindex at Kakioka. The training was conducted using the data obtained for the period from 1960 to 1984. The method was validated for the period from 1985 to 2003. The trained network can be used for daily forecasting ionospheric variations including storms using prompt daily reports of K-index, sunspot number, and solar flux values available on-line.

References

  • Altinay, O., E. Tulunay, and Y. Tulunay, Forecasting of ionospheric critical frequency using neural networks, Geophys. Res. Lett., 24(12), 1467–1470, 1997.

    Article  Google Scholar 

  • Barkhatov, N. A. and S. E. Renynov, Forecasting of the critical frequency of the ionosphere F2 layer by the method of artificial neural networks, Int. J. Geomagn. Aeron., 5, GI2010, 2004.

    Article  Google Scholar 

  • Bilitza, D., K. Rawer, L. Bossy, and T. Gulyaeva, International reference ionosphere-past, present, future, Adv. Space Res., 13, 3–23, 1993.

    Article  Google Scholar 

  • Cander, L. R. and X. Lamming, Neural networks in ionospheric prediction and short term forecasting, 10th International Conference on Antennas and Propagation, IEEE Conf. Publ., 436, 27–30, 1997.

    Google Scholar 

  • Funahashi, K., On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183–192, 1989.

    Article  Google Scholar 

  • Haykin, S., Neural networks, a comprehensive foundation, Prentice Hall Inc., Upper Saddle River, NJ, 1999.

    Google Scholar 

  • Jones, K. L. and H. Rishbeth, The origin of storm increases of mid-latitude F-layer electron concentration, J. Atmos. Terr. Phys., 33, 391–401, 1971.

    Article  Google Scholar 

  • Koike, K., Statistics of the K-index, Kakioka Magnetic Observatory, Gijutsu Hokoku, 31, 32–46, 1991.

    Google Scholar 

  • Kolmogorov, A. N., On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Doklady Akademii Nauk SSSR, 144, 679–681, 1957.

    Google Scholar 

  • Lei J. H., L. B. Liu, W. X. Wan, and S. R. Zhang, Modeling investigation of ionospheric storm effects over Millstone Hill during August 4–5, 1992, Earth Planets Space, 56(9), 903–908, 2004.

    Article  Google Scholar 

  • Martyn, D. F., The morphology of the ionospheric variations associated with magnetic disturbance, Proc. R. Soci. London, Ser. A, 218, 1–18, 1953.

    Article  Google Scholar 

  • Matsushita, S., A study of the morphology of ionospheric storms, J. Geophys. Res., 13, 305–321, 1959.

    Article  Google Scholar 

  • Matuura, N., Theoretical models of ionospheric storms, Space Sci. Rev., 13, 124–189, 1972.

    Article  Google Scholar 

  • McKinnell, L. A. and A. W. V. Poole, Predicting the ionospheric F layer using neural networks, J. Geophys. Res., 109, doi:10.1029/2004JA0104445, 2004.

  • Oyeyemi, E. O., A. W. V. Poole, and L. A. McKinnell, On the global model for foF2 using neural networks, Radio Sci., 40, doi:10.1029/2004RS003223, 2005a.

  • Oyeyemi, E. O., A. W. V. Poole, and L. A. McKinnell, On the global short-term forecasting of the ionospheric critical frequency foF2 up to 5 hours in advance using neural networks, Radio Sci., 40, doi:10.1029/2004RS003239, 2005b.

  • Prölss, G. W., Ionospheric F-region storms, in Handbook of Atmospheric Electrodynamics, vol. 2, (Ed) Volland, H., edited by Boca Raton, 195–248 pp., CRC Press, 1995.

    Google Scholar 

  • Rishbeth, H., S. Ganguly, and J. C. G. Walker, Field-aligned and fieldperpendicular velocities in the ionospheric F2-layer, J. Atmos. Terr. Phys., 40, 767–784, 1978.

    Article  Google Scholar 

  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams, Learning representation by back-propagation errors, Nature, 323, 533–536, 1986.

    Article  Google Scholar 

  • Torr, D. G. and M. R. Torr, Chemistry of the thermosphere and ionosphere, J. Atmos. Terr. Phys., 41, 797–839, 1979.

    Article  Google Scholar 

  • Tulunay, A. K. E., I. Topalli, and Y. Tulunay, Temporal and spatial forecasting of ionospheric critical frequency using neural networks, Radio Sci., 34, 1497–1506, 1999.

    Article  Google Scholar 

  • Williscroft, L. A. and A. W. V. Poole, Neural networks, foF2, sunspot number and magnetic activity, Geophys. Res. Lett., 23, 3659–3662, 1996.

    Article  Google Scholar 

  • Wintoft, P. and Lj. R. Cander, Short-term prediction of foF2 using timedelay neural network, Phys. Chem. Earth, 24, 343–347, 1999.

    Google Scholar 

  • Wintoft, P. and Lj. R. Cander, Ionospheric foF2 storm forecasting using neural networks, Phys. Chem. Earth, 25, 267–273, 2000.

    Google Scholar 

  • Yumura, T., On the «three-hour-range Indices» K at Kakioka, Memoirs of the Kakioka magnetic observatory, 6, 1–17, 1951.

    Google Scholar 

  • Yonezawa, T., Theory of formation of the ionosphere, Space Sci. Rev., 5, 3–56, 1966.

    Article  Google Scholar 

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Correspondence to Maho I. Nakamura.

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Nakamura, M.I., Maruyama, T. & Shidama, Y. Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan. Earth Planet Sp 59, 1231–1239 (2007). https://doi.org/10.1186/BF03352071

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  • DOI: https://doi.org/10.1186/BF03352071

Key words

  • Ionospheric forecast
  • ionospheric storms
  • neural network
  • foF2