A flood episode in Northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions.

Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (Limited-area Ensemble Prediction System). The meteorological models are coupled with a distributed rainfall-runoff model (TOP- KAPI) to simulate the discharge of the Reno River (Northern Italy), for a recent severe weather episode affecting Northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over the entire Northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that both mesoscale model ensembles remarkably outperform the global ensemble for application at basin scale as the horizontal resolution plays a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides more informative probabilistic predictions with respect to COSMO-LEPS, since it is characterized by a larger spread especially at short lead times. A thorough analysis of the multi-model results shows that this behaviour is due to the different characteristics of the involved meteorological models and represents the added value of the multi-model approach. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members, forced by the same large scale conditions, indicates that the systems are governed mainly by the large scale boundary conditions. [ABSTRACT FROM AUTHOR]