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www.nonlin-processes-geophys.net/9/477/2002/
doi:10.5194/npg-9-477-2002
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Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics
Aristotelian University of Thessaloniki, Dept of Electrical and Computers Eng., 54006 Thessaloniki, Greece
Abstract. In this work, Neural-Network-based single-station hourly daily foF2 and M(3000)F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964- 1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000)F2. The first foF2 model employs the E-layer local noon calculated daily critical frequency (foE12) and the local noon F2- layer critical frequency of the previous day. The second foF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12), and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000)F2 model employs the E-layer local noon calculated daily critical frequency (foE12), its ± 3 h deviations and the local noon cosine of the solar zenith angle (cos c12). The second model, which introduces a new M(3000)F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12), and the previous day F2-layer critical frequency measured at Juliusruh at noon.
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Citation: Xenos, T. D.: Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics, Nonlin. Processes Geophys., 9, 477-486, doi:10.5194/npg-9-477-2002, 2002. Bibtex EndNote Reference Manager XML
