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Advances in Meteorology
Volume 2017, Article ID 9204081, 11 pages
Research Article

The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

1Israeli Hydrological Service, Jerusalem, Israel
2University of Haifa, Haifa, Israel
3Israeli Meteorological Service, Beit Dagan, Israel
4Ben Gurion University, Beersheba, Israel
5Climate Prediction Center, NCEP, College Park, MD, USA

Correspondence should be addressed to Amir Givati; li.vog.retaw@grima

Received 11 March 2017; Revised 31 May 2017; Accepted 13 June 2017; Published 31 July 2017

Academic Editor: Pedro Jiménez-Guerrero

Copyright © 2017 Amir Givati et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME) models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME). The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.