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  • Autor
    • McKinnell, Lee-Anne
    • Friedrich, Martin
  • TitelA Neural Network Based Ionospheric Model for the Auroral Zone
  • Datei
  • DOI10.1016/j.jastp.2007.05.003
  • Persistent Identifier
  • Erschienen inJournal of atmospheric and solar-terrestrial physics
  • Band69
  • Erscheinungsjahr2007
  • Heft12
  • Seiten1459-1470
  • LicenceCC-BY
  • ZugriffsrechteCC-BY
  • Download Statistik220
  • Peer ReviewJa
  • AbstractA new empirical model for the lower ionosphere in the auroral zone, called IMAZ, has been developed, tested and refined for use in the International Reference Ionosphere (IRI) global model. Available ionospheric data have been used to train neural networks (NNs) to predict the high latitude electron density profile. Data from the European Incoherent Scatter Radar (EISCAT), based near Tromsø, Norway (69.581N, 19.231E), combined with rocket-borne measurements (from 611 to 691 geomagnetic latitude) make up the database of reliable D- and E-region data. NNs were trained with different combinations of the following input parameters: day number, time of day, total absorption, local magnetic K index, planetary Ap index, 10.7 cm solar radio flux, solar zenith angle and pressure surface. The output that the NNs were trained to predict was the electron density for a given set of input parameters. The criteria for determining the optimum NN are (a) the root mean square (RMS) error between the measured and predicted output values, and (b) the ability to reproduce the absorption they are meant to represent. An optimum input space was determined and then adapted to suit the requirements of the IRI community. In addition, the true quiet electron densities were simulated and added to the database, thus allowing the final model to be valid for riometer absorptions down to 0 dB. This paper discusses the development of a NN-based model for the high-latitude, lower ionosphere, and presents results from the version developed specifically for the IRI user community.