Methods for Observing Atmospheric Turbulence with Uncrewed Aerial Systems (UAS)

The planetary boundary layer (the first 1–2 km above the surface) is the most dynamic and turbulent layer of Earth’s atmosphere, contributing the greatest uncertainty to weather forecasting. However, the current global meteorological observing system severely lacks coverage above the ground—inhibiting predictability. New measurement technologies in the form of un-crewed aerial vehicles or ‘drones’ are a favourable solution with lower cost, greater reusability and the ability to observe additional atmospheric variables compared with existing systems. One such variable is turbulence, which is important to the fields of atmospheric convection, air quality and crewed/un-crewed aviation.

In this project, you will examine methods for quantifying atmospheric turbulence with UAS; specifically, you will work with industry-sponsors Menapia Ltd. who are developing an autonomous UAS for operational meteorology. The project is open-ended, meaning you can approach the challenge with several methods, and compare their performance. Extensive data collection in both a laboratory setting and in UK field campaign deployments will increase your scientific skillset and allow you to design experiments through which to test the validity of your approach(es). Data analysis will be a central part of the project and develop your expertise in both traditional turbulence analysis techniques as well as novel machine learning approaches. Quantifying the limitations and uncertainties of each approach is key. A stretch goal would be to develop a calibration technique for UAS-measured atmospheric turbulence.