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

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A neural network-based foF2 model for a single station in the polar cap. / Athieno, R.; Jayachandran, P. T.; Themens, D. R.

In: Radio Science, Vol. 52, No. 6, 01.06.2017, p. 784-796.

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Athieno, R. ; Jayachandran, P. T. ; Themens, D. R. / A neural network-based foF2 model for a single station in the polar cap. In: Radio Science. 2017 ; Vol. 52, No. 6. pp. 784-796.

Bibtex

@article{4d84cffcbf184649a69ebcd22efd138d,
title = "A neural network-based foF2 model for a single station in the polar cap",
abstract = "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).",
keywords = "empirical modeling, HF propagation, ionosphere, neural networks",
author = "R. Athieno and Jayachandran, {P. T.} and Themens, {D. R.}",
year = "2017",
month = jun,
day = "1",
doi = "10.1002/2016RS006192",
language = "English",
volume = "52",
pages = "784--796",
journal = "Radio Science",
issn = "0048-6604",
publisher = "American Geophysical Union",
number = "6",

}

RIS

TY - JOUR

T1 - A neural network-based foF2 model for a single station in the polar cap

AU - Athieno, R.

AU - Jayachandran, P. T.

AU - Themens, D. R.

PY - 2017/6/1

Y1 - 2017/6/1

N2 - 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).

AB - 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).

KW - empirical modeling

KW - HF propagation

KW - ionosphere

KW - neural networks

UR - http://www.scopus.com/inward/record.url?scp=85021319073&partnerID=8YFLogxK

U2 - 10.1002/2016RS006192

DO - 10.1002/2016RS006192

M3 - Article

AN - SCOPUS:85021319073

VL - 52

SP - 784

EP - 796

JO - Radio Science

JF - Radio Science

SN - 0048-6604

IS - 6

ER -