A neural network-based foF2 model for a single station in the polar cap

R. Athieno*, P. T. Jayachandran, D. R. Themens

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


A neural network (NN) model has been developed for the critical frequency of the F2 layer (foF2) at Resolute (74.70°N, 265.10°E) using data obtained from the Space Physics Interactive Data Resource (no longer available) for the period between 1975 and 1995. This model is a first step toward addressing the discrepancies of the International Reference Ionosphere (IRI) foF2 or peak electron density (NmF2) at high latitudes recently presented by Themens et al. (2014). The performance of the NN model has been evaluated using foF2 data obtained from the Canadian Advanced Digital Ionosonde at Resolute (74.75°N, 265.00°E) for the period between 2009 and 2013, in comparison with the IRI predictions. The 2012 version and the International Union of Radio Science option of IRI have been used. The NN nighttime monthly median foF2 variation demonstrates good agreement with observations compared to the IRI. The NN model is able to reproduce the enhancements in foF2 during the equinoxes and also shows an improvement during disturbed days. Root mean square errors were computed between hourly and monthly median model predictions and observations, and on the whole, the NN model seems to perform better during low solar activity and the equinoxes. The NN model shows an improvement in performance on average by 26.638% for hourly foF2 and 32.636% for monthly median foF2, compared to 7.877% obtained for the same station by the most recent NN model that attempted to predict foF2 at a polar cap station (Oyeyemi and Poole, 2005).

Original languageEnglish
Pages (from-to)784-796
Number of pages13
JournalRadio Science
Issue number6
Publication statusPublished - 1 Jun 2017


  • empirical modeling
  • HF propagation
  • ionosphere
  • neural networks

ASJC Scopus subject areas

  • Condensed Matter Physics
  • General Earth and Planetary Sciences
  • Electrical and Electronic Engineering


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