Evaluation of two convection‐permitting ensemble systems in the HyMeX Special Observation Period (SOP1) framework

Abstract

This study compares and evaluates two convection‐permitting ensemble systems based on Consortium for Small‐scale Modeling (COSMO) and Applications of Research to Operations at Mesoscale (AROME) models in the HyMeX framework. The performance of both AROME‐EPS and COSMO‐H2‐EPS (where EPS denotes Ensemble Prediction System) is assessed over the whole HyMeX special observation period SOP1. Afterwards, an analysis of the predictability of two heavy precipitation events observed during the intense observation period IOP16a of the first HyMeX special observation period SOP1 on 26 October 2012 is also undertaken. Ensemble discharge forecasts were carried out to reinforce the quantitative precipitation forecast evaluation.A probabilistic evaluation is conducted over a 53 day period of the HyMeX SOP1. AROME‐EPS has a more discriminating behaviour than COSMO‐H2‐EPS, especially when comparing both ensembles over a verification domain including a strong variability in precipitation events. AROME‐EPS still has slightly better reliability, but the statistical resolution is nearly the same for both convection‐permitting ensemble prediction systems (CPEPS).For the specific case of heavy precipitation occurring over the Var region (southeastern France), the fine‐scale surface precipitation prediction is strongly sensitive to the good behaviour of a surface low pressure simulated by the ensembles, focusing strong low‐level moisture towards Var. The convergence between strong moistened southerly low‐level inflow and northerly cold air blowing from the Po Valley is a key factor controlling the predictability of the heavy precipitation episode over the Liguria region (northwestern Italy). The two convection‐permitting ensembles, though different in their characteristics, exhibit a good amount of probabilistic skill in forecasting heavy precipitation at a relatively high spatial and temporal resolution. Therefore they can be regarded as promising tools for operational forecasts.