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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 8, issue 1
Adv. Sci. Res., 8, 33–37, 2012
https://doi.org/10.5194/asr-8-33-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 8, 33–37, 2012
https://doi.org/10.5194/asr-8-33-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Mar 2012

21 Mar 2012

Comparing different meteorological ensemble approaches: hydrological predictions for a flood episode in Northern Italy

S. Davolio1, T. Diomede2, C. Marsigli2, M. M. Miglietta3,4, A. Montani2, and A. Morgillo2 S. Davolio et al.
  • 1Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy
  • 2HydroMeteoClimate Regional Service of ARPA Emilia Romagna, Bologna, Italy
  • 3Institute of Atmospheric Sciences and Climate, National Research Council, Lecce, Italy
  • 4Institute of Ecosystem Study, National Research Council, Verbania Pallanza, Italy

Abstract. Within the framework of coupled meteorological-hydrological predictions, this study aims at comparing two high-resolution meteorological ensembles, covering short and medium range. The two modelling systems have similar characteristics, as almost the same number of members, the model resolution (about 7 km), the driving ECMWF global ensemble prediction system, but are obtained through different methodologies: the former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter follows a single-model approach, based on COSMO-LEPS (Limited-area Ensemble Prediction System), the operational ensemble forecasting system developed within the COSMO consortium.

Precipitation forecasts are evaluated in terms of hydrological response, after coupling the meteorological models with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno river (Northern Italy), for a severe weather episode.

Although a single case study does not allow for robust and definite conclusions, the comparison among different predictions points out a remarkably better performance of mesoscale model ensemble forecasts compared to global ones. Moreover, the multi-model ensemble outperforms the single model approach.

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