@InProceedings{GomesJśniorTcheSouzChou:2022:BiCoAn,
               author = "Gomes J{\'u}nior, S{\'e}rgio Pinto and Tcheou, Michel Pompeu and 
                         Souza Filho, Jo{\~a}o Baptista de Oliveira and Chou, Sin Chan",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         do Estado do Rio de Janeiro (UERJ)} and {Universidade Federal do 
                         Rio de Janeiro (UFRJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "A bias correction analysis for SST data from BESM in the tropical 
                         South Atlantic Ocean",
                 year = "2022",
         organization = "Workshop em Modelagem Num{\'e}rica de Tempo, Clima e 
                         Mudan{\c{c}}as Clim{\'a}ticas Usando o Modelo Eta: Aspectos 
                         F{\'{\i}}sicos e Num{\'e}ricos (WorEta), 7.",
            publisher = "INPE",
             keywords = "bias correction, sea surface temperature, BESM.",
             abstract = "Sea surface temperature (SST) is an important variable that drives 
                         climate. Tropical Atlantic Ocean SST variability has a strong 
                         influence on the distribution of precipitation in South America, 
                         including northeastern Brazil and the southwestern Amazon region. 
                         Therefore, to seek for more accurate rainfall forecasts in those 
                         regions, our work aimed at evaluating different bias correction 
                         statistical methods to be applied onto the SST data predicted by 
                         the Brazilian Earth System Model (BESM). The reference 
                         observations were obtained from the ERA5 database. The chosen 
                         region to produce corrected SST is within the coordinates 30W-10E 
                         and 20S-0. The first method evaluated was the basic Quantile 
                         Mapping (QM), considering its wide adoption, ability to deal with 
                         higher order moments, and computationally efficiency. However, it 
                         has some drawbacks, such as requiring the same number of samples 
                         in the time series observed and the one generated by BESM to 
                         ensure an accurate distribution mapping. In addition, QM also 
                         assumes that the error correction function for the modeled and 
                         observed distributions are stationary or time invariant. To 
                         address the first limitation, we evaluate the modified QM method. 
                         Furthermore, since the stationarity assumption of QM models may be 
                         not suitable for long-term observation series, this experimental 
                         study also includes the Scaled Distribution Mapping (SDM) method. 
                         Records are daily based and span the period from November 1980 to 
                         October 2010. The bias correction procedures were individually 
                         applied to each model grid point at the study region. This 
                         investigation indicates the existence of some correlation between 
                         spatially contiguous areas and best performing methods.",
  conference-location = "Online",
      conference-year = "26-30 set. 2022",
                  ibi = "8JMKD3MGP3W34T/47MGJMB",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34T/47MGJMB",
           targetfile = "TO_03_A1_GomesJuniorSP.pdf",
        urlaccessdate = "20 maio 2024"
}