Space weather presents a threat to human activities such as Global Navigation Satellite System (GNSS) positioning and timing, power systems, radio communications and transpolar aviation. Nowcasts and forecasts of the ionosphere could help mitigate some of these damaging effects. In this thesis, state-of-the-art ionospheric specification techniques are assessed in a long-term study. That study shows that Global Positioning System (GPS) derived tomographic images specify monthly median ionospheric Total Electron Content (TEC) accurately in Europe and North America throughout the twelve-year test period. Following this assessment, developments are presented in three key areas. The resolution of horizontal structures in ionospheric images over Africa is assessed. The accuracy gains from adding receivers are quantified using a simulation approach, showing that an extended GPS network reduces Root-Mean-Square (RMS) error from 9.5 TEC units for the currently operational network to 4.5 TEC units. A fictional, ideal network is demonstrated to produce images with RMS errors of 3.0 TEC units. Images of the vertical electron density distribution, vital for High Frequency (HF) radio operators, are greatly improved by adding observations of the ionospheric vertical profile to an imaging algorithm that relies on GPS observations. The peak electron density is resolved to an RMS accuracy of 0.5 x 1011 electrons/m3, compared to an RMS accuracy of 1.0 x 1011 electrons/m3 for the standard approach. A novel experimental method is employed to show that forecasts of ionospheric storms could benefit significantly from accurate specification of the initial neutral composition, in particular the ratio of O to N2 . A theoretical experiment shows that an ideal assimilation of the thermospheric composition can improve storm-time forecasts by at least 10% for over 19 hours, whilst an ideal ionospheric assimilation improves forecasts for less than four hours. This finding will aid the development of a coupled thermosphere ionosphere forecast system.
|Date of Award||19 Dec 2013|
|Supervisor||Cathryn Mitchell (Supervisor) & David Jackson (Supervisor)|