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| Titel |
An Ionospheric Index Model based on Linear Regression and Neural Network Approaches |
| VerfasserIn |
Mpho Tshisaphungo, Lee-Anne McKinnell, John Bosco Habarulema |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250138762
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| Publikation (Nr.) |
 EGU/EGU2017-1878.pdf |
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| Zusammenfassung |
| The ionosphere is well known to reflect radio wave signals in the high frequency (HF) band
due to the present of electron and ions within the region. To optimise the use of long distance
HF communications, it is important to understand the drivers of ionospheric storms and
accurately predict the propagation conditions especially during disturbed days. This paper
presents the development of an ionospheric storm-time index over the South African region
for the application of HF communication users. The model will result into a valuable tool to
measure the complex ionospheric behaviour in an operational space weather monitoring and
forecasting environment. The development of an ionospheric storm-time index is based on a
single ionosonde station data over Grahamstown (33.3∘S,26.5∘E), South Africa.
Critical frequency of the F2 layer (foF2) measurements for a period 1996-2014 were
considered for this study. The model was developed based on linear regression
and neural network approaches. In this talk validation results for low, medium and
high solar activity periods will be discussed to demonstrate model’s performance. |
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