@Article{RodriguesDoblCoel:2014:MuCaCo,
               author = "Rodrigues, L. R. L. and Doblas-Reyes, F. J. and Coelho, Caio 
                         Augusto dos Santos",
          affiliation = "Institut Catal{\`a} de Ci{\`e}ncies del Clima (IC3), Doctor 
                         Trueta 203, Barcelona, 08005, Spain and Institut Catal{\`a} de 
                         Ci{\`e}ncies del Clima (IC3), Doctor Trueta 203, Barcelona, 
                         08005, Spain; Instituci{\'o} Catalana de Recerca i Estudis 
                         Avan{\c{c}}ats (ICREA), Passeig Llu{\'{\i}}s Companys 23, 
                         Barcelona, 08010, Spain and Centro de Previs{\~a}o de Tempo e 
                         Estudos Clim{\'a}ticos, Instituto Nacional de Pesquisas Espaciais 
                         (CPTEC/INPE), Rodovia Presidente Dutra Km 40, Cachoeira Paulista, 
                         12630-000, Brazil",
                title = "Multi-model calibration and combination of tropical seasonal sea 
                         surface temperature forecasts",
              journal = "Climate Dynamics",
                 year = "2014",
               volume = "42",
               number = "3-4",
                pages = "597--616",
             keywords = "seasonal prediction.",
             abstract = "Different combination methods based on multiple linear regression 
                         are explored to identify the conditions that lead to an 
                         improvement of seasonal forecast quality when individual 
                         operational dynamical systems and a statistical-empirical system 
                         are combined. A calibration of the post-processed output is 
                         included. The combination methods have been used to merge the 
                         ECMWF System 4, the NCEP CFSv2, the M{\'e}t{\'e}o-France System 
                         3, and a simple statistical model based on SST lagged regression. 
                         The forecast quality was assessed from a deterministic and 
                         probabilistic point of view. SSTs averaged over three different 
                         tropical regions have been considered: the Niņo3.4, the 
                         Subtropical Northern Atlantic and Western Tropical Indian SST 
                         indices. The forecast quality of these combinations is compared to 
                         the forecast quality of a simple multi-model (SMM) where all 
                         single models are equally weighted. The results show a large range 
                         of behaviours depending on the start date, target month and the 
                         index considered. Outperforming the SMM predictions is a difficult 
                         task for linear combination methods with the samples currently 
                         available in an operational context. The difficulty in the robust 
                         estimation of the weights due to the small samples available is 
                         one of the reasons that limit the potential benefit of the 
                         combination methods that assign unequal weights. However, these 
                         combination methods showed the capability to improve the forecast 
                         reliability and accuracy in a large proportion of cases. For 
                         example, the Forecast Assimilation method proved to be competitive 
                         against the SMM while the other combination methods outperformed 
                         the SMM when only a small number of forecast systems have skill. 
                         Therefore, the weighting does not outperform the SMM when the SMM 
                         is very skilful, but it reduces the risk of low skill situations 
                         that are found when several single forecast systems have a low 
                         skill.",
                  doi = "10.1007/s00382-013-1779-8",
                  url = "http://dx.doi.org/10.1007/s00382-013-1779-8",
                 issn = "0930-7575",
                label = "scopus",
             language = "en",
        urlaccessdate = "03 maio 2024"
}