Lightning is an extremely energetic electric discharge process in our atmosphere. Lightning activity is used as an indicator for the nowcasting of severe weather, significantly affects atmospheric chemistry, and threatens electrical and electronic devices. Yet, our fundamental understanding of atmospheric electricity is far from complete. For example, new processes above thunderclouds have been discovered which are collectively known as Transient Luminous Events (TLEs) and Terrestrial Gamma ray Flashes (TGFs). This PhD project aims to characterize and improve on a thunderstorm detector recently developed by private industry to warn of local lightning activity at airports. Most existing lightning location systems rely on sub-ionospheric propagating radio waves. The new technique measures the displacement currents induced on an electrode exposed to changes in the atmosphere’s electric field between 1-45 Hz, associated with all forms of lightning and wind-blown space charge. The unique dataset provided by this instrument has been investigated to (1) design a noise rejection and waveform recognition algorithm based on machine learning methods; (2) identify the source of anomalously strong current transients detected in fair-weather and their link to TLEs; (3) provide the first multi-instrumental analysis of thunderstorms producing superbolts and TLEs in the UK and northern Europe; (4) study the contribution of global lightning activity and associated Schumann Resonances (SR) on the quasi-electrostatic currents measured at Portishead (UK) during a 5-year period. This work indicates that the technique can be profitably used to prevent the risk posed to humans and infrastructures by intense lightning discharges, and additionally suggests a broad range of new applications for this method in atmospheric electricity research.
|Date of Award||23 Mar 2022|
|Supervisor||Alec J. Bennett (Supervisor), Martin Fullekrug (Supervisor) & Biagio Forte (Supervisor)|
- atmospheric electricity
- machine learning