Ionospheric Modelling for GNSS, Precise Point Positioning, PNT and Space Weather Resilience

Modern society depends on Global Navigation Satellite Systems (GNSS) such as GPS and Galileo. These systems support navigation, timing, telecommunications, transport, surveying, finance, energy networks, emergency response, agriculture and autonomous systems. Many of these applications rely on Positioning, Navigation and Timing (PNT) services being accurate, available and trustworthy. However, GNSS signals must pass through the ionosphere, a charged region of the upper atmosphere that is strongly affected by space weather. Variations in ionospheric electron density, total electron content (TEC), scintillation and travelling ionospheric disturbances can delay, distort or disrupt GNSS signals. This PhD will develop improved ionospheric modelling for precise point positioning (PPP), resilient PNT and GNSS space weather applications.

The ionosphere is both scientifically fascinating and operationally important. It responds to solar radiation, geomagnetic storms, auroral activity, atmospheric waves and coupling from the lower atmosphere. For GNSS users, the ionosphere can be a major error source. For single-frequency receivers, ionospheric delay must be modelled or corrected. For high-precision PPP and PPP-RTK, ionospheric information affects convergence time, accuracy, integrity and reliability. During disturbed space weather, standard corrections may become less accurate, and users may not know when the system has become degraded. This creates a need for models that are not only accurate on average, but also able to describe uncertainty, identify risk and support robust decision-making.

This project will investigate how ionospheric models and observations can be used to improve GNSS positioning and PNT resilience. Possible research directions include developing regional or global ionospheric corrections, modelling uncertainty in PPP applications, detecting ionospheric gradients and irregularities, studying scintillation impacts, or designing validation methods for space weather-aware PNT services. The project could combine physics-based ionospheric modelling, empirical models, data assimilation, statistical learning, signal processing and large-scale analysis of GNSS receiver data.

The practical questions are clear: How well can we nowcast or forecast ionospheric delay for GNSS users? Which ionospheric structures matter most for PPP accuracy? Can we produce corrections that are useful during geomagnetic storms? How can we warn users when the ionosphere is likely to degrade PNT performance? What information should be provided to aviation, maritime, surveying, autonomous systems or critical infrastructure users? How can ionospheric science support more resilient alternatives and backups to GNSS-dependent services?

A major strength of this PhD is its breadth. It links space weather science to a technology used every day by billions of people. You will gain training in ionospheric physics, GNSS data analysis, precise positioning, scientific computing, uncertainty quantification and applied modelling. The project would suit a student from physics, mathematics, engineering, computer science, geophysics, data science or a related discipline. Prior experience with GNSS or ionospheric modelling would be useful but is not essential. More important is curiosity, quantitative ability and enthusiasm for applying science to real-world systems.

The outcomes of the project could support better ionospheric corrections, faster and more reliable PPP, improved space weather services, and stronger PNT resilience for critical systems. As GNSS dependency grows and space weather risks become more visible, there is an urgent need for people who understand both the physics of the ionosphere and the engineering needs of users. This PhD will prepare you to work at that interface, developing models and tools that help make navigation and timing systems more accurate, robust and trustworthy.

Funding notes:
Please make an informal enquiry to Prof. Sean Elvidge ([email protected]) before making a formal application.