@Article{CoelhoStDoBaGuOl:2006:SeFoRe,
               author = "Coelho, Caio Augusto dos Santos and Stephenson, David B. and 
                         Doblas-Reyes, Francisco J. and Balmaseda, Magdalena and Guetter, 
                         A. and Oldenborgh, G. J.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Department 
                         of Meteorology, University of Reading, Earley Gate and {European 
                         Centre for Medium-Range Weather Forecasts (ECMWF)} and {European 
                         Centre for Medium-Range Weather Forecasts (ECMWF)} and Instituto 
                         Tecnol{\'o}gico SIMEPAR, Centro Polit{\'e}cnico da UFPR and 
                         {Royal Dutch Meteorological Institute}",
                title = "A bayesian approach for multi-model downscaling: seasonal 
                         forecasting of regional rainfall and river flows in south 
                         America",
              journal = "Meteorological Applications",
                 year = "2006",
               volume = "13",
               number = "01",
                pages = "73--82",
                month = "Mar.",
             keywords = "multi-model downscaling, regional rainfall, river flow, south 
                         America, bayesian approach, seasonal forecasting.",
             abstract = "This study addresses three issues: spatial downscaling, 
                         calibration, and combination of seasonal predictions produced by 
                         different coupled ocean-atmosphere climate models. It examines the 
                         feasibility of using a Bayesian procedure for producing combined, 
                         well-calibrated downscaled seasonal rainfall forecasts for two 
                         regions in South America and river flow forecasts for the ParanŽa 
                         river in the south of Brazil and the Tocantins river in the north 
                         of Brazil. These forecasts are important for national electricity 
                         generation management and planning. A Bayesian procedure, referred 
                         to here as forecast assimilation, is used to combine and calibrate 
                         the rainfall predictions produced by three climate models. 
                         Forecast assimilation is able to improve the skill of 3-month lead 
                         November-December-January multi-model rainfall predictions over 
                         the two South American regions. Improvements are noted in forecast 
                         seasonal mean values and uncertainty estimates. River flow 
                         forecasts are less skilful than rainfall forecasts. This is 
                         partially because natural river flow is a derived quantity that is 
                         sensitive to hydrological as well as meteorological processes, and 
                         to human intervention in the form of reservoir management.",
           copyholder = "SID/SCD",
                  doi = "10.1017/S1350482705002045",
                  url = "http://dx.doi.org/10.1017/S1350482705002045",
                 issn = "1350-4827",
             language = "en",
           targetfile = "Coelho.Bayesian.pdf",
                  url = "http://journals.cambridge.org",
        urlaccessdate = "20 set. 2024"
}