GNSS signals at high latitudes: The effect of ionospheric scintillation

Global Navigation Satellite Systems (GNSS) have become a ubiquitous technology, with 5.8 billion devices in use in 2017 world-wide[1]. Innovate UK has described GNSS as "the invisible utility", directly supporting sectors generating a total of £206bn in Gross Value Added (11.3% of UK GDP)[2]. However this report also highlighted that GNSS is "subject to various vulnerabilities to failure". This PhD studentship tackles one of these vulnerabilities, namely the threat posed to GNSS by disturbances in the ionosphere.
The ionosphere, the part of the atmosphere made up of plasma, is a highly complex medium containing electron density structures with a wide range of spatial scale sizes. Small scale structures (with a horizontal dimension of less than tens of km) can cause scintillation of signals used by GNSS. This routinely reduces the positional accuracy and can limit the service availability and integrity. Whilst the processes driving these structures are well understood (i.e. the influence of solar activity, the solar wind etc.), the relative importance of these driving processes is a fundamental, unanswered question. Models to predict these structures are urgently needed.
The purpose of this PhD studentship is to develop modelling methods and models, to determine the relative importance of the driving processes and to predict ionospheric scintillation. The members of the supervisory team have extensive experience in this field, with a track record of publications in leading journals[3, 4, 5, 6] and have been awarded a best paper prize at a leading international meeting in the field[7]. The student will begin by undertaking model development. Specifically they will assimilate data from the neutral atmosphere into the Advanced European electron density (Ne) Assimilation System (AENeAS) model. This will then be used to drive models to predict plasma structures. The student will compare machine learning techniques to determine the optimum modelling method. They will determine the relative importance of the driving processes and will apply statistical tests to evaluate the quality of the model predictions.
In addition to their scientific work, the student will be a full and active member of the research group. They will undertake appropriate professional development tasks to ensure that, upon completion of their PhD, they are well prepared for a career in scientific research in academia or in industry.