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@Article{AndradeCoelCava:2019:GlPrHi,
               author = "Andrade, Felipe Marques de and Coelho, Caio Augusto dos Santos and 
                         Cavalcanti, Iracema Fonseca de Albuquerque",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Global precipitation hindcast quality assessment of the 
                         Subseasonal to Seasonal (S2S) prediction project models",
              journal = "Climate Dynamics",
                 year = "2019",
               volume = "52",
               number = "9/10",
                pages = "5451--5475",
                month = "May",
             keywords = "Subseasonal prediction, S2S prediction project models, Hindcast 
                         quality, Precipitation, Teleconnections.",
             abstract = "This study assessed subseasonal global precipitation hindcast 
                         quality from all Subseasonal to Seasonal (S2S) prediction project 
                         models. Deterministic forecast quality of weekly accumulated 
                         precipitation was verified using different metrics and hindcast 
                         data considering lead times up to 4weeks. The correlation scores 
                         were found to be higher during the first week and dropped as lead 
                         time increased, confining meaningful signals in the tropics mostly 
                         due to El Nino-Southern Oscillation and Madden-Julian 
                         Oscillation-related effects. The contribution of these two 
                         phenomena to hindcast quality was assessed by removing their 
                         regressed precipitation patterns from predicted fields. The 
                         model's rank showed ECMWF, UKMO, and KMA as the top scoring models 
                         even when using a single control member instead of the mean of all 
                         ensemble members. The lowest correlation was shared by CMA, ISAC, 
                         and HMCR for most weeks. Models with larger ensemble sizes 
                         presented noticeable reduction in correlation when subsampled to 
                         fewer perturbed members, showing the value of ensemble prediction. 
                         Systematic errors were measured through bias and variance ratio 
                         revealing in general large positive (negative) biases and variance 
                         overestimation (underestimation) over the tropical oceans 
                         (continents and/or extratropics). The atmospheric circulation 
                         hindcast quality was also examined suggesting the importance of 
                         using a relatively finer spatial resolution and a coupled model 
                         for resolving the tropical circulation dynamics, particularly for 
                         simulating tropical precipitation variability. The extratropical 
                         circulation hindcast quality was found to be low after the second 
                         week likely due to the inherent unpredictability of the 
                         extratropical variability and errors associated with model 
                         deficiencies in representing teleconnections.",
                  doi = "10.1007/s00382-018-4457-z",
                  url = "http://dx.doi.org/10.1007/s00382-018-4457-z",
                 issn = "0930-7575",
             language = "en",
           targetfile = "Andrade2019_Article_GlobalPrecipitationHindcastQua.pdf",
        urlaccessdate = "05 dez. 2020"
}


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