An analysis of automatically scaled F1 layer data over Grahamstown, South Africa
- Jacobs, Linda, Poole, Allon W V, McKinnell, Lee-Anne
- Authors: Jacobs, Linda , Poole, Allon W V , McKinnell, Lee-Anne
- Date: 2004
- Language: English
- Type: text , Article
- Identifier: vital:6808 , http://hdl.handle.net/10962/d1004194
- Description: This paper describes an analysis of automatically scaled F1 layer data over Grahamstown, South Africa (33.3°S, 26.5°E). An application for real time raytracing through the South African ionosphere was identified, and for this application real time evaluation of the electron density profile is essential. Raw real time virtual height data are provided by a Lowell Digisonde (DPS), which employs the automatic scaling software, ARTIST whose output includes the virtual-to-real height data conversion. Experience has shown that there are times when the raytracing performance is degraded because of difficulties surrounding the real time characterisation of the F1 region by ARTIST. The purpose of this investigation is to establish the extent of the problem, the times and conditions under which it occurs, with a view to formulating remedial alternative strategies, such as predictive modelling.
- Full Text:
- Date Issued: 2004
- Authors: Jacobs, Linda , Poole, Allon W V , McKinnell, Lee-Anne
- Date: 2004
- Language: English
- Type: text , Article
- Identifier: vital:6808 , http://hdl.handle.net/10962/d1004194
- Description: This paper describes an analysis of automatically scaled F1 layer data over Grahamstown, South Africa (33.3°S, 26.5°E). An application for real time raytracing through the South African ionosphere was identified, and for this application real time evaluation of the electron density profile is essential. Raw real time virtual height data are provided by a Lowell Digisonde (DPS), which employs the automatic scaling software, ARTIST whose output includes the virtual-to-real height data conversion. Experience has shown that there are times when the raytracing performance is degraded because of difficulties surrounding the real time characterisation of the F1 region by ARTIST. The purpose of this investigation is to establish the extent of the problem, the times and conditions under which it occurs, with a view to formulating remedial alternative strategies, such as predictive modelling.
- Full Text:
- Date Issued: 2004
Neural network-based ionospheric modelling over the South African region
- McKinnell, Lee-Anne, Poole, Allon W V
- Authors: McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2004
- Language: English
- Type: Article
- Identifier: vital:6795 , 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.
- Full Text:
- Date Issued: 2004
- Authors: McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2004
- Language: English
- Type: Article
- Identifier: vital:6795 , 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.
- Full Text:
- Date Issued: 2004
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