Accurate and timely weather forecasts save lives and livelihoods and are important to a host of industries from energy to agriculture and retail. Critical to the success of these forecasts is the understanding and representation of the physical processes occurring within weather systems, from isolated convection to winter storms, and the characteristics of their predictability. Modern-day operational weather forecasts are performed at a range of model resolutions and time ranges with the most computationally-demanding being convective-scale ensemble forecasts: multiple forecasts at resolutions capable of representing individual convective systems. Such forecasts enable predictions of the probability of weather events to be made for localised regions (e.g. the probability of rain tomorrow afternoon in Reading). The interpretation and use of such models lead to many associated challenges. Additionally, the longer timescale predictions (e.g. the likelihood of a severe storm hitting Reading in 7 days) required for risk mitigation are dependent on the accurate representation of previous weather systems and interactions over large scales (1000s of km). Our research aims to tackle these problems.
Specific areas of current interest include:
- Exploiting the benefits of convective-scale forecasts and improving ensemble spread characteristics
- The impact of coupling ocean and wave models to atmospheric models at the convective scale
- The characteristics of Arctic weather systems and the importance of Arctic-midlatitude interactions for predictability in the midlatitudes
- The role and representation of diabatic (moist) processes in weather systems and associated barriers to predictability
- The characteristics of, and processes leading to, low-level wind jets in midlatitude winter storms
These topics can be addressed using experiments with state-of-the-art weather forecast models, operational forecast model output and observations (including from field campaigns).
This PhD is an opportunity to contribute to these, or related, areas of novel research, with the precise focus of the PhD dependent on the applicant and their interests/aptitude.
This is a self funded project.
Applicants should hold, or be predicted, a strong undergraduate degree (2:1 UK honours degree or equivalent), or Masters (merit or distinction level), in a physical or mathematical science.
To discuss this PhD opportunity informally please contact Prof. Sue Gray ([email protected]).