- Title
- Neural network-based ionospheric modelling over the South African region
- Creator
- McKinnell, Lee-Anne
- Creator
- Poole, Allon W V
- Date Issued
- 2004
- Date
- 2004
- Type
- Article
- Identifier
- vital:6795
- Identifier
- http://hdl.handle.net/10962/d1003839
- Description
- During the past decade, South African scientists have pioneered research in the field of ionospheric modelling using the technique of neural networks (NNs). Global ionospheric models have always been insufficient for the South African region owing to an historical paucity of available data. Within the past 10 years, however, three new ionospheric sounders have been installed locally and are operating continuously. These sounders are located at Grahamstown (33.3°S, 26.5°E), Louisvale (28.5°S, 21.2°E) and Madimbo (22.4°S, 30.9°E). The addition of a modern sounder at Grahamstown enlarged the ionospheric database for this station to 30 years, making this archive a considerable asset for ionospheric research. Quality control and online availability of the data has also added to its attraction. An important requirement for empirical modelling, but especially for employing NNs, is a large database describing the history of the relationship between the ionosphere and the geophysical parameters that define its behaviour. This review describes the path of South African ionospheric modelling over the past 10 years, the role of NNs in this development, the international collaborations that have arisen from this, and the future of ionospheric modelling in South Africa.
- Format
- 5 pages
- Format
- Language
- English
- Relation
- McKinnell, L-A. and Poole, A.W.V. (2004) Neural network-based ionospheric modelling over the South African region. South African Journal of Science, 100 (11 & 12). pp. 519-523.
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Collections
Poole, Allon (Prof)
McKinnell, Lee-Anne (Dr)
RU Department of Physics and Electronics
SDG 09. Industry, Innovation and Infrastructure
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