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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. [ABSTRACT FROM AUTHOR]

Abstract: With the aim of developing of an ensemble forecasting system for the short‐range, the validity of a multi‐analysis multi‐model approach for initial‐ and boundary‐condition perturbations has been explored by the Consortium for Small‐scale Modelling (COSMO). This approach has been adopted in the COSMO Short‐Range Ensemble Prediction System (COSMO–SREPS), in which members are driven by a few operational deterministic runs. In this work, we show the extent to which this approach is beneficial with respect to the downscaling of a single‐model global ensemble, adopted by the operational COSMO Limited‐Area Ensemble Prediction System (COSMO–LEPS). Both COSMO–SREPS and COSMO–LEPS are made up of 16 integrations of the COSMO model with 7 km horizontal mesh size. Results based on the 2010–2011 winter season show that the 16 member COSMO–LEPS outperforms the 16 member COSMO–SREPS, which has boundaries perturbed by three global model runs only, but the reverse is true when the three‐member versions of the two systems are compared. Hence, if only a small‐size Limited‐Area Model (LAM) ensemble is feasible, then the multimodel approach for boundaries gives better results. The dependence of the scores on ensemble size is also studied, showing that all the quality measures tend to saturate before the full size is reached. Finally, the impact of combining the two ensembles is assessed, showing that a positive impact results from adding multi‐model‐driven members to COSMO–LEPS. [ABSTRACT FROM AUTHOR]

Analyzes the influence of the soil moisture initialization on predicting rainfall in the limited-area model of Bologna (LAMBO) for the flash flood episode that occurred during September 1995 in the South Ticino area (Switzerland). Results indicating that the precipitation field obtained for the flood episode in the second experiment is closer to the observations than in the control run; How the precipitation peak modelled on South Ticino was closer to the observations; Results confirming the importance of the correct initialization of the soil moisture field even in cases of short-range forecasting.