Jonathan Coney
- Email: mm16jdc@leeds.ac.uk
- Thesis title: Use of Artificial Intelligence to understand mountain weather and climate processes
- Supervisors: Dr Andrew Ross, Dr Leif Denby, He Wang (Computing), Simon Vosper (Met Office), Annelize van Niekerk (ECMWF), Tom Dunstan (Met Office)
Profile
I am a fourth year PhD student in the Institute of Climate and Atmospheric Science (ICAS) in the School of Earth and Environment at the University of Leeds. I am part of the 2020 cohort of the NERC Panorama Doctoral Training Partnership (DTP).
My PhD project investigates whether Artificial Intelligence (AI) can be utilised to improve the understanding of complex flows over orography (hills and mountains) and improve their representation in models. The flow in mountainous regions is complex and poorly represented in global and weather and climate models currently, and AI techniques could be an exciting opportunity to make further progress in the representation of orographic effects in weather models.
I completed a BSc in Mathematics, followed by an MRes in Climate and Atmospheric Science at the University of Leeds. My MRes project involed investigating the effectiveness of crowdsourcing data from Netatmo weather stations in the UK. I worked with Ben Pickering, David Dufton and Maryna Lukach (more details here).
Web Links
Publications
- Identifying and characterising trapped lee waves using deep learning techniques
- How useful are crowdsourced air temperature observations? An assessment of Netatmo stations and quality control schemes over the United Kingdom
Research interests
- Mountain Weather
- Atmospheric and Cloud Dynamics
- Atmospheric Science
Qualifications
- BSc Mathematics (Leeds)
- MRes Climate and Atmospheric Science (Leeds)
Research groups and institutes
- Institute for Climate and Atmospheric Science
- Atmospheric and Cloud Dynamics