@InProceedings{RepelliNobr:1998:StPrSe,
               author = "Repelli, Carlos Alberto and Nobre, Paulo",
          affiliation = "{CPTEC-INPE-Cachoeira Paulista-12630-000-SP-Brasil}",
                title = "Statistical prediction of sea suface temperature over the Tropical 
                         Atlantic",
            booktitle = "Anais...",
                 year = "1998",
         organization = "Congresso Brasileiro de Meteorologia, 10.",
             abstract = "A statistical system to predict sea surface temperature anomalies 
                         (SSTA) over the tropical oceans, with emphasis on the tropical 
                         Atlantic, is described. Canonical correlation analysis (CCA) is 
                         used to identify critical sequences of predictor patterns which 
                         tend to evolve into subsequent patterns, and that can be used to 
                         form a forecast. The results indicate that SST fields over the 
                         equatorial Pacific and tropical Atlantic can be a potential 
                         predictor of the SSTA over the tropical Atlantic with three to 
                         four months in advance. The spatial structures of the SSTA over 
                         the tropical Atlantic for the period March-April-May are well 
                         captured by the predictions done with initial conditions from 
                         September to February. Model performance is better over the 
                         northern tropical Atlantic than over the southern tropical 
                         Atlantic, where persistence is hardly beaten. Results of this work 
                         can contribute to improve seasonal climate predictions of rainfall 
                         anomalies over the Northeast Brazil region.",
  conference-location = "Brasilia",
      conference-year = "26-30 out. 1998",
           copyholder = "SID/SCD",
             language = "en",
         organisation = "SBMET",
           targetfile = "Repelli_Statistical prediction.pdf",
        urlaccessdate = "02 maio 2024"
}