@Article{GuimarãesCWKBFBS:2021:InPeAs,
               author = "Guimar{\~a}es, Bruno dos Santos and Coelho, Caio Augusto dos 
                         Santos and Woolnough, Steven James and Kubota, Paulo Yoshio and 
                         Bastarz, Carlos Frederico and Figueroa, Silvio Nilo and Bonatti, 
                         Jos{\'e} Paulo and Souza, Dayana Castilho de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Reading} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "An inter-comparison performance assessment of a Brazilian global 
                         sub-seasonal prediction model against four sub-seasonal to 
                         seasonal (S2S) prediction project models",
              journal = "Climate Dynamics",
                 year = "2021",
               volume = "56",
               number = "7/8",
                pages = "2359--2375",
                month = "Aprl",
             keywords = "Sub-seasonal prediction, Forecast verification, Intraseasonal 
                         variability, Madden-Julian oscillation.",
             abstract = "This paper presents an inter-comparison performance assessment of 
                         the newly developed Centre for Weather Forecast and Climate 
                         Studies (CPTEC) model (the Brazilian Atmospheric Model version 
                         1.2, BAM-1.2) against four sub-seasonal to seasonal (S2S) 
                         prediction project models from: Japan Meteorological Agency (JMA), 
                         Environmental and Climate Change Canada (ECCC), European Centre 
                         for Medium-range Weather Forecasts (ECMWF) and Australian Bureau 
                         of Meteorology (BoM). The inter-comparison was performed using 
                         hindcasts of weekly precipitation anomalies and the daily 
                         evolution of Madden-Julian Oscillation (MJO) for 12 extended 
                         austral summers (November-March, 1999/2000-2010/2011), leading to 
                         a verification sample of 120 hindcasts. The deterministic 
                         assessment of the prediction of precipitation anomalies revealed 
                         ECMWF as the model presenting the highest (smallest) correlation 
                         (root mean squared error, RMSE) values among all examined models. 
                         JMA ranked as the second best performing model, followed by ECCC, 
                         CPTEC and BoM. The probabilistic assessment for the event 
                         {"}positive precipitation anomaly{"} revealed that ECMWF presented 
                         better discrimination, reliability and resolution when compared to 
                         CPTEC and BoM. However, these three models produced overconfident 
                         probabilistic predictions. For MJO predictions, CPTEC crosses the 
                         0.5 bivariate correlation threshold at around 19 days when using 
                         the mean of 4 ensemble members, presenting similar performance to 
                         BoM, JMA and ECCC. Overall, CPTEC proved to be competitive 
                         compared to the S2S models investigated, but with respect to ECMWF 
                         there is scope to improve the prediction system, likely by a 
                         combination of including coupling to an interactive ocean, 
                         improving resolution and model parameterization schemes, and 
                         better methods for ensemble generation.",
                  doi = "10.1007/s00382-020-05589-5",
                  url = "http://dx.doi.org/10.1007/s00382-020-05589-5",
                 issn = "0930-7575",
             language = "en",
           targetfile = "Guimar{\~a}es2021_Article_AnInter-comparisonPerformanceA.pdf",
        urlaccessdate = "09 maio 2024"
}