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


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.


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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).

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Key words

  • Ionospheric forecast
  • ionospheric storms
  • neural network
  • foF2