Exploiting the benefits of convection-permitting ensembles

Gainford, A. (2026) Exploiting the benefits of convection-permitting ensembles. PhD thesis, University of Reading. doi: 10.48683/1926.00128862

Abstract/Summary

The development and use of convection-permitting ensembles (CPEs) within op erational meteorological centres has improved the quality of guidance available for decision making, especially for precipitation. The explicit representation of km scale details provides better quantitative predictions of hazardous convection than models that must rely on parametrizations. Despite these benefits, long-standing spread deficiencies limit the usefulness of CPE outputs, restricting their full poten tial. Therefore, efforts to elucidate CPE characteristics can be highly beneficial for both operational and research purposes. Here, three studies are presented that link the behaviour of CPEs run over the UK to the synoptic regime, which provides a source of predictability, using the Met Office’s weather forecast models. These stud ies focus on analysing precipitation forecasts, which we expect to benefit the most from operating at convective scales. It has long been understood that CPEs do not possess enough spread in the placement of precipitation. In the first study, the effect of blending the more accurate large-scale initial conditions of the global model into the CPE analysis is assessed, demonstrating that the spatial spread-skill relationship is improved by a modest but significant amount. Similarly, it is also well known that the synoptic-scale variability introduced through the driving ensemble can have a large influence on the evolution of nested CPE forecasts. The second study quantifies the driving-ensemble influence, which is strongest under mobile regimes and weakest under conditionally unstable regimes. To facilitate these studies, new spatial analysis methods, including the parent-child Fractions Skill Score, have been developed that provide broader insights into the behaviour of CPE forecasts. In the final study, an experimental post processing system driven by spatial methods is applied to CPE forecasts to assess the value of clustering members based on the co-location of precipitation features. CPE clustering is shown to be a reliable tool, and provides the most value when targeted over regions that will be impacted by hazardous convection.

Item Type	Thesis (PhD)
URI	https://centaur.reading.ac.uk/id/eprint/128862
Identification Number/DOI	10.48683/1926.00128862
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Date on Title Page	September 2025
Download/View statistics	View download statistics for this item