Predictability of orographic convection: high-resolution ensembles from the Unified Model

Accurate forecasting of convective precipitation over complex terrain is important because of the substantial flooding that can be caused by the intense precipitation. In principle, the predictability of convection should be increased over orography, where localized vertical motions owing to specific topographic features can remove the convective inhibition and trigger cells. At present however, the forecasting skill for heavy convective showers and thunderstorms over orography is low. This is partly due to poor understanding of the thermally-driven orographic flow of moisture and aerosols which feed the storm. Additional uncertainties arise from parameterised sub-gridscale processes within the model as many of the small-scale features that initiate convection are either completely unresolved (e.g. boundary layer thermals) or highly uncertain (e.g. surface fluxes), even in high resolution models.

The availability of data collected during the Convective and Orographically-induced Precipitation Study (COPS) provides a unique opportunity to study the role of orography in determining the predictability of convection. Accurate prediction of the location of convective initiation requires high-resolution grids which can represent the fine-scale terrain details. In this study, we conduct high-resolution simulations using the UK Met Office Unified Model over the COPS region to quantify the predictability of severe convective storms encountered during COPS. An ensemble approach is taken using initial and boundary data from the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Results are presented for two cases: one where there is weak synoptic forcing and convection was triggered by local convergence; and a second where convection was triggered by a squall line produced in the outflow boundary of a weak Mesoscale Convective System (MCS). For the case with weak synoptic forcing the ensemble spread from an initial conditions ensemble is small. This case shows sensitivity to parameters such as soil moisture and horizontal diffusion, demonstrating that it is also important to capture the uncertainty in the surface forcing and the parameterisation schemes. In contrast, for the synoptically-driven case MOGREPS produces an ensemble that better encompasses the observations.