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@Article{RodriguesDoblCoel:2018:CaCoMo,
               author = "Rodrigues, Luis Ricardo Lage and Doblas-Reyes, Francisco J. and 
                         Coelho, Caio Augusto dos Santos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Barcelona 
                         Supercomputing Center-Centro Nacional de Supercomputaci{\'o}n} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Calibration and combination of monthly near-surface temperature 
                         and precipitation predictions over Europe",
              journal = "Climate Dynamics",
                 year = "2018",
               volume = "1",
                pages = "1",
             keywords = "Climate prediction · Multimodel ensemble · Forecast quality 
                         assessment · Forecast assimilation.",
             abstract = "A Bayesian method known as the Forecast Assimilation (FA) was used 
                         to calibrate and combine monthly near-surface temperature and 
                         precipitation outputs from seasonal dynamical forecast systems. 
                         The simple multimodel (SMM), a method that combines predictions 
                         with equal weights, was used as a benchmark. This research focuses 
                         on Europe and adjacent regions for predictions initialized in May 
                         and November, covering the boreal summer and winter months. The 
                         forecast quality of the FA and SMM as well as the single seasonal 
                         dynamical forecast systems was assessed using deterministic and 
                         probabilistic measures. A non-parametric bootstrap method was used 
                         to account for the sampling uncertainty of the forecast quality 
                         measures. We show that the FA performs as well as or better than 
                         the SMM in regions where the dynamical forecast systems were able 
                         to represent the main modes of climate covariability. An 
                         illustration with the near-surface temperature over North 
                         Atlantic, the Mediterranean Sea and Middle-East in summer months 
                         associated with the well predicted first mode of climate 
                         covariability is offered. However, the main modes of climate 
                         covariability are not well represented in most situations 
                         discussed in this study as the seasonal dynamical forecast systems 
                         have limited skill when predicting the European climate. In these 
                         situations, the SMM performs better more often.",
                  doi = "10.1007/s00382-018-4140-4",
                  url = "http://dx.doi.org/10.1007/s00382-018-4140-4",
                 issn = "0930-7575",
                label = "lattes: 4978912302419377 3 RodriguesDoblCoel:2018:CaCoMo",
             language = "en",
           targetfile = "rodrigues_calibration.pdf",
        urlaccessdate = "29 nov. 2020"
}


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