1Agenzia per la Protezione dell’Ambiente e per i Servizi Tecnici, APAT, Rome, Italy
2Departament d’Astronomia i Meteorologia, Facultat de Física, Universitat de Barcelona, Barcelona, Spain
3Laboratorio di Meteorologia e Modellistica Ambientale, LaMMA, Sesto Fiorentino, Florence, Italy
4CNR-Istituto di Scienze dell’Atmosfera e del Clima, CNR-ISAC, Bologna, Italy
5Agenzia Regionale Prevenzione e Ambiente dell’Emilia-Romagna, Servizio Idro Meteo, ARPA-SIM, Bologna, Italy
6Servizio Agrometeorologico Regionale per la Sardegna, SAR, Sassari, Italy
7Departament de Física, Universitat de les Illes Balears, Palma de Mallorca, Spain
Abstract. In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event.
The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered.
For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts.
High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.