Maximising the value of observations for forecasting intense rainfall

With increasing computer power, the resolutions at which we are able to forecast the weather are ever increasing. We are now closer to achieving the ambition of resolving fine (100 metre)-scale convection allowing for accurate forecasts of intense and damaging rainfall. However, increasing resolution alone will not ensure accurate forecasts. Routine assimilation of meteorological observations has long proven to be essential to the accuracy of numerical weather prediction, allowing for numerical models of the atmosphere to stay in line with reality as seen by the observations. Data assimilation will be even more essential, although much more challenging, at the convective scales due to the increased sensitivity to small perturbations. The challenges we will focus on are two-fold: The rapid development of convection means that the assimilation must be performed at very short intervals (e.g. every 30 minutes versus every 6 hours at the global scales), yet very high-resolution observations are needed to constrain the models, leading to a need to process a large volume of observations in a very short space of time.

This project will advance the field of convective-scale data assimilation by developing new methods to efficiently assimilate the large quantity of high-frequency and high-resolution observations needed to constrain convective-scale models. We will utilise concepts from information theory to identify the most valuable observations as the convection evolves and develop novel techniques to compress the data with minimum amount of information loss.

Eligibility requirements: Applicants should hold, or be predicted, a strong undergraduate degree (2:i 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 Dr Alison Fowler ([Email Address Removed]) or Prof Bob Plant ( [Email Address Removed])