Sensitivities of a squall line over central Europe in a convective-scale ensemble

Accurate forecasting of convective storms 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 IOP 9c (20th July 2007) where convection was triggered by a squall line produced in the outflow boundary of a weak Mesoscale Convective System (MCS). In this case, the high resolution ensemble is shown to be sensitive to errors on the low-resolution grid, which propagate in through the lateral boundaries. Ensemble sensitivity analysis is used to link the mesoscale response of the high-resolution ensemble members to the differences in the large-scale driving conditions.