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@Article{GublerSBACEJMSSS:2020:AsECSE,
               author = "Gubler, S. and Sedlmeier, K. and Bhend, J. and Avalos, G. and 
                         Coelho, Caio Augusto dos Santos and Escajadillo, Y. and 
                         Jacques-Coper, M. and Martinez, R. and Schwierz, C. and Skansi, M. 
                         de and Spirig, C. H.",
          affiliation = "Federal Office of Meteorology and Climatology, MeteoSwiss and 
                         Federal Office of Meteorology and Climatology, MeteoSwiss and 
                         Federal Office of Meteorology and Climatology, MeteoSwiss and 
                         {Servicio Nacional de Meteorolog{\'{\i}}a e Hidrolog{\'{\i}}a 
                         del Per{\'u}} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Servicio Nacional de Meteorolog{\'{\i}}a e 
                         Hidrolog{\'{\i}}a del Per{\'u}} and {Universidad de 
                         Concepci{\'o}n} and {Centro Internacional para la 
                         Investigaci{\'o}n del Fen{\'o}meno de El Niņo} and Federal 
                         Office of Meteorology and Climatology, MeteoSwiss and Servicio 
                         Meteorol{\'o}gico Nacional, Buenos Aires and Federal Office of 
                         Meteorology and Climatology, MeteoSwiss",
                title = "Assessment of ECMWF SEAS5 seasonal forecast performance over South 
                         America",
              journal = "Weather and Forecasting",
                 year = "2020",
               volume = "35",
               number = "2",
                pages = "561--584",
                month = "Apr.",
             abstract = "Seasonal predictions have a great socioeconomic potential if they 
                         are reliable and skillful. In this study, we assess the prediction 
                         performance of SEAS5, version 5 of the seasonal prediction system 
                         of the European Centre for Medium-Range Weather Forecasts (ECMWF), 
                         over South America against homogenized station data. For 
                         temperature, we find the highest prediction performances in the 
                         tropics during austral summer, where the probability that the 
                         predictions correctly discriminate different observed outcomes is 
                         70%. In regions lying to the east of the Andes, the predictions of 
                         maximum and minimum temperature still exhibit considerable 
                         performance, while farther to the south in Chile and Argentina the 
                         temperature prediction performance is low. Generally, the 
                         prediction performance of minimum temperature is slightly lower 
                         than for maximum temperature. The prediction performance of 
                         precipitation is generally lower and spatially and temporally more 
                         variable than for temperature. The highest prediction performance 
                         is observed at the coast and over the highlands of Colombia and 
                         Ecuador, over the northeastern part of Brazil, and over an 
                         isolated region to the north of Uruguay during DJF. In general, 
                         Niņo-3.4 has a strong influence on both air temperature and 
                         precipitation in the regions where ECMWF SEAS5 shows high 
                         performance, in some regions through teleconnections (e.g., to the 
                         north of Uruguay). However, we show that SEAS5 outperforms a 
                         simple empirical prediction based on Niņo-3.4 in most regions 
                         where the prediction performance of the dynamical model is high, 
                         thereby supporting the potential benefit of using a dynamical 
                         model instead of statistical relationships for predictions at the 
                         seasonal scale.",
                  doi = "10.1175/WAF-D-19-0106.1",
                  url = "http://dx.doi.org/10.1175/WAF-D-19-0106.1",
                 issn = "0882-8156",
             language = "en",
           targetfile = "gubler_assessment.pdf",
        urlaccessdate = "13 abr. 2021"
}


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