@InProceedings{CardosoSilv:2006:AsFoPe,
               author = "Cardoso, Andrea de Oliveira and Silva Dias, Pedro Leite",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Instituto 
                         de Astronomia Geof{\'{\i}}sica e Ci{\^e}ncias 
                         Atmosf{\'e}ricas, Departamento de Ci{\^e}ncias 
                         Atmosf{\'e}ricas, Universidade de S{\~a}o Paulo (IAG/ USP)",
                title = "Assessing Forecast Performance of the empirical model to forecast 
                         precipitation in the South and Southeast Regions of Brazil",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Vera, Carolina and Nobre, Carlos",
                pages = "517--520",
         organization = "International Conference on Southern Hemisphere Meteorology and 
                         Oceanography, 8. (ICSHMO).",
            publisher = "American Meteorological Society (AMS)",
              address = "45 Beacon Hill Road, Boston, MA, USA",
             keywords = "SST, empirical model, forecast , precipitation, South Brazil, 
                         Southeast Brazil.",
             abstract = "Empirical models can provide reliable forecasts through the 
                         knowledge of the conceptual relationship between the predictand 
                         and predictors. An empirical model of monthly precipitation 
                         forecast (predictand) in South and Southeast Brazilian is 
                         presented in this paper, based on estimates of the variability of 
                         the Atlantic Ocean (OA) and Pacific Ocean (OP) sea surface 
                         temperature (SST) as predictors. The data sets of SST and 
                         precipitation were reduced in dimension: (a) A cluster analysis 
                         defined the monthly precipitation regions with homogeneous 
                         characteristics and (b) The rotated principal component analysis 
                         allowed to reduce the dimension of SST time series. The empirical 
                         model, based on linear regression analysis, was designed to 
                         forecast the average precipitation in homogeneous regions based on 
                         the time series of the scores of the principal components of SST 
                         as predictors. Lagged forecasts up to 4 months were performed. The 
                         results indicate similarities in the skill of the empirical model 
                         using SST modes with different lags. The SST scores constitute a 
                         robust set of predictors of the precipitation mainly over the SE 
                         Brazil, pointing out a significant contribution of modes with high 
                         amplitudes in the tropical and subtropical OP, completed by the 
                         SST variations in the subtropical belt of the South Atlantic. The 
                         positive extreme values of precipitation were under-estimated by 
                         the empirical model in all homogeneous regions. In the case of the 
                         South Region of Brazil the precipitation also are over-estimated. 
                         The annual cycle and the low frequency variations are well 
                         captured by the empirical model.The best performance of the model 
                         is obtained in the Southeastern region of Brazil. The empirical 
                         model forecasts are better than the climatological forecasts in 
                         all rain categories in the homogeneous regions.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "24-28 Apr. 2006",
           copyholder = "SID/SCD",
             language = "en",
         organisation = "American Meteorological Society (AMS)",
                  ibi = "cptec.inpe.br/adm_conf/2005/10.31.19.23",
                  url = "http://urlib.net/ibi/cptec.inpe.br/adm_conf/2005/10.31.19.23",
           targetfile = "517-520.pdf",
                 type = "Climate predictions",
        urlaccessdate = "24 abr. 2024"
}