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| Titel |
A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results |
| VerfasserIn |
J. B. Habarulema, L.-A. McKinnell, B. D. L. Opperman |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 27, no. 5 ; Nr. 27, no. 5 (2009-05-07), S.2111-2125 |
| Datensatznummer |
250016529
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| Publikation (Nr.) |
 copernicus.org/angeo-27-2111-2009.pdf |
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| Zusammenfassung |
| This paper attempts to describe the search for the parameter(s) to represent
solar wind effects in Global Positioning System total electron content (GPS
TEC) modelling using the technique of neural networks (NNs). A study is
carried out by including solar wind velocity (Vsw), proton number
density (Np) and the Bz component of the interplanetary magnetic field
(IMF Bz) obtained from the Advanced Composition Explorer (ACE) satellite
as separate inputs to the NN each along with day number of the year (DN),
hour (HR), a 4-month running mean of the daily sunspot number (R4) and the
running mean of the previous eight 3-hourly magnetic A index values (A8).
Hourly GPS TEC values derived from a dual frequency receiver located at
Sutherland (32.38° S, 20.81° E), South Africa for 8 years
(2000–2007) have been used to train the Elman neural network (ENN) and the
result has been used to predict TEC variations for a GPS station located at
Cape Town (33.95° S, 18.47° E). Quantitative results indicate
that each of the parameters considered may have some degree of influence on
GPS TEC at certain periods although a decrease in prediction accuracy is also
observed for some parameters for different days and seasons. It is also
evident that there is still a difficulty in predicting TEC values during
disturbed conditions. The improvements and degradation in prediction
accuracies are both close to the benchmark values which lends weight to the
belief that diurnal, seasonal, solar and magnetic variabilities may be the
major determinants of TEC variability. |
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