An investigation into improved ionospheric F1 layer predictions over Grahamstown, South Africa

Abstract

This thesis describes an analysis of the F1 layer data obtained from the Grahamstown (33.32°S, 26.500 E), South Africa ionospheric station and the use of this data in improving a Neural Network (NN) based model of the F1 layer of the ionosphere. An application for real-time ray tracing 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-toreal height data conversion. Experience has shown that there are times when the ray tracing performance is degraded because of difficulties surrounding the real-time characterization of the F1 region by ARTIST. Therefore available DPS data from the archives of the Grahamstown station were re-scaled manually in order to establish the extent of the problem and the times and conditions under which most inaccuracies occur. The re-scaled data were used to update the F1 contribution of an existing NN based ionospheric model, the LAM model, which predicts the values of the parameters required to produce an electron density profile. This thesis describes the development of three separate NNs required to predict the ionospheric characteristics and coefficients that are required to describe the F1 layer profile. Inputs to the NNs include day number, hour and measures of solar and magnetic activity. Outputs include the value of the critical frequency of the F1 layer, foF1, the real height of reflection at the peak, hmFl, as well as information on the state of the F1 layer. All data from the Grahamstown station from 1973 to 2003 was used to train these NNs. Tests show that the predictive ability of the LAM model has been improved by incorporating the re-scaled data.