Environmental controls on convective-scale perturbation growth

Flooding from intense rainfall, resulting from convection, causes millions of pounds of damage each year. However, convection has limited predictability, often resulting in short lead times for warnings of such events. This research aims to determine the spatial scales of perturbation growth in convective-scale forecasts for different environmental conditions over the British Isles. The convective adjustment timescale identifies whether cases are in equilibrium with the large-scale forcing, so can be used to quantitatively classify convection into the regimes of quasi-equilibrium and non-equilibrium. A method is found to calculate the timescale based on criteria considering its variability and the environment in which it is calculated. The most appropriate method uses a Gaussian kernel to spatially smooth convective available potential energy and precipitation accumulation fields before the calculation. A model climatology is created over the summers of 2012-2014 (due to limited operational data from the United Kingdom Variable resolution, UKV, configuration of the Met Office United Model) to understand the characteristics of the regimes over the British Isles. In summer 85% of convection is in convective quasi-equilibrium, with more nonequilibrium events in the south and west of the British Isles. The UKV is perturbed with Gaussian buoyancy perturbations to create an ensemble. These perturbations represent unresolved processes within the boundary layer. The perturbation growth is examined across a spectrum of cases and it is shown that events at the non-equilibrium end of the spectrum have higher spatial predictability than those at the equilibrium end (O(1 km) vs. O(10 km)), implying more localised growth in nonequilibrium, than quasi-equilibrium, environments. This research has implications in the interpretation of forecasts for defining regions when issuing weather warnings associated with convection. The research also has implications for adaptive forecasting, in which high-resolution forecasts are used for nonequilibrium convection and large-member ensembles are used for events in convective quasi-equilibrium