Thermosphere Modelling for Satellite Drag, Collision Avoidance and Space Weather Resilience

Low Earth orbit is becoming increasingly crowded. Scientific spacecraft, Earth observation missions, communication satellites and large commercial constellations all depend on accurate knowledge of where satellites are now and where they will be in the future. One of the largest sources of uncertainty is the upper atmosphere. Even hundreds of kilometres above the surface of the Earth, satellites experience drag from the neutral thermosphere. During space weather events, solar extreme ultraviolet radiation, geomagnetic storms, Joule heating and particle precipitation can heat and expand the thermosphere, increasing atmospheric density and changing satellite orbits. This PhD will develop improved thermosphere modelling for satellite drag, orbit prediction, collision avoidance and space traffic management.

The thermosphere is a highly dynamic region of the near-Earth space environment. It is coupled to the ionosphere below and the magnetosphere above, responds to solar activity, and varies with season, local time, latitude, longitude and geomagnetic conditions. Small errors in thermospheric density can grow into significant errors in satellite position, particularly for objects in low Earth orbit. This matters for satellite operators, space surveillance networks, launch planning, re-entry prediction, conjunction assessment and space domain awareness. It also matters scientifically: the thermosphere is one of the least directly observed parts of the atmosphere, and major geomagnetic storms provide natural experiments in how energy from the Sun is deposited into the Earth system.

This project will explore how thermosphere models can be improved, validated and used for practical satellite drag applications. Depending on the student’s interests, the work could include physics-based modelling, empirical modelling, uncertainty quantification, data assimilation, machine learning, ensemble forecasting, model validation using satellite orbit data, or the development of operationally useful drag products. Observations may include satellite accelerometer data, GNSS orbit residuals, space weather indices, solar radio flux, geomagnetic activity measures, ionospheric data and publicly available catalogues of satellite orbits.

Key research questions include: How accurately can we forecast thermospheric density during quiet and disturbed space weather? Which solar and geomagnetic inputs matter most for satellite drag? Can we detect when a model is becoming unreliable? How should uncertainty be represented in orbit prediction? How can thermosphere models support collision avoidance decisions without giving false confidence? What happens to drag forecasts during extreme geomagnetic storms?

The PhD will sit at the interface between space weather, atmospheric physics, orbital mechanics and applied data science. You will develop skills in scientific programming, model evaluation, statistical analysis, satellite orbit data, space environment modelling and communication with technical users. The project is suitable for students from physics, mathematics, engineering, computer science, geophysics or a related quantitative discipline. You do not need to arrive as an expert in satellite drag or thermosphere science; the project is designed for a motivated student who wants to learn across disciplines and work on a problem with real-world importance.

This is an exciting time to work in this field. The growth of low Earth orbit mega-constellations, the increasing need for space sustainability, and the impact of recent geomagnetic storms have made satellite drag forecasting a high-priority challenge. By the end of this PhD, you will have contributed to better models of the upper atmosphere and helped improve the resilience, safety and sustainability of satellite operations in a space weather-affected environment.

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