Physics
Scientific paper
Apr 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010jastp..72..509h&link_type=abstract
Journal of Atmospheric and Solar-Terrestrial Physics, Volume 72, Issue 5-6, p. 509-520.
Physics
3
Tec Models, Statistical Validation, Magnetic Storms, Neural Networks
Scientific paper
This paper presents the results from a study designed to investigate the ability of a newly developed neural network (NN) based model to follow total electron content (TEC) dynamics over the Southern African region. The investigation is carried out by comparing results from the NN model with actual TEC data derived from Global Positioning System (GPS) observations and TEC values predicted by the International Reference Ionosphere (IRI-2007) model during magnetic storm periods over Southern Africa. The magnetic storm conditions chosen for the study presented in this paper occurred during the periods 16-21 April 2002, 1-6 October 2002, and 28 October-01 November 2003. A total of six South African GPS stations were used for the validation of the two models during these periods. A statistical analysis of the comparison between the actual TEC behaviour and that predicted by the two models is shown. In addition, ionosonde measurements from the South African Louisvale (28.5°S, 21.2°E) station, located close to one of the validation GPS stations used, are also considered during the Halloween storm period of 28-31 October 2003. The generalisation of TEC behaviour by the NN model is demonstrated by producing predicted TEC maps during magnetic storm periods over South Africa. Presented results demonstrate the ability of NNs in predicting TEC variability over South Africa during magnetically disturbed conditions, and highlight areas for improvement.
Habarulema John Bosco
McKinnell Lee-Anne
Opperman Ben D. L.
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