@InProceedings{PaesOlivFranSart:2009:CoTeSu,
               author = "Paes, Rosa Cristhyna de Oliveira Vieira and Oliveira, Ant{\^o}nio 
                         do Nascimento and Fran{\c{c}}a, Gutemberg Borges and Sartori 
                         Neto, Angelo",
          affiliation = "UFRJ and UFRJ and UFRJ and Petrobras",
                title = "Composi{\c{c}}{\~a}o de temperatura da superf{\'{\i}}cie do 
                         mar (TSM) para assimila{\c{c}}{\~a}o em modelos num{\'e}ricos 
                         de circula{\c{c}}{\~a}o oce{\^a}nica",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7031--7038",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "sea surface temperature, remote sensing, GOES, temperatura da 
                         superf{\'{\i}}cie do mar, sensoriamento remoto.",
             abstract = "This work presents a method for obtaining the optimum average of 
                         Sea Surface Temperature (SST) data, without cloud contamination, 
                         obtained hourly from GOES satellite data, aiming at the 
                         assimilation of SST field in a numerical model of oceanic 
                         circulation. It knew that SST field obtained by remote sensed data 
                         is an important tool for identifying the oceanographic features, 
                         such as eddies, thermal fronts and others; however, it is also 
                         well know that cloud cover is the main limitation for that. In 
                         order to minimize such cloud cover problem, the methodology 
                         proposed is based on the behavior of the variation of SST field in 
                         time. The idea is to fill the SST pixel of image with nearest 
                         historical data. The results show that is possible to get those 
                         pixels with data from 20 days ago taking into account that the 
                         accuracy of SST GOES algorithm is ±1.0°C. The Results have showed 
                         that the final product of SST composition of 48 hours is able to 
                         identify mesoscale oceanographic features.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.14.18.59",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.14.18.59",
           targetfile = "7031-7038.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "03 maio 2024"
}