@InProceedings{RosaQuadPezz:2017:UtImOr,
               author = "Rosa, Eliana Bertol and Quadro, M{\'a}rio Francisco Leal de and 
                         Pezzi, Luciano Ponzi",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Utiliza{\c{c}}{\~a}o de imagens orbitais de radia{\c{c}}{\~a}o 
                         de onda longa para identifica{\c{c}}{\~a}o da Zona de 
                         Converg{\^e}ncia do Atl{\^a}ntico Sul",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7635--7642",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The South Atlantic Convergence Zone (SACZ) directly influence the 
                         pluviometric indices in the Amazon, midwest and southeast regions 
                         of Brasil, supporting economical activities like agriculture and 
                         energy production by hydroelectric. When anomalous conditions 
                         affect the SACZ formation, it can be responsible for natural 
                         disasters like floods and landslides, as well as drought 
                         conditions. For these reasons it is extremely important understand 
                         and predict this meteorological system. In this sense, the present 
                         work intends to use outgoing longwave radiation orbital images as 
                         an indicative of the presence of the SACZ. An classification 
                         algorithm was developed and the results show that the agreement 
                         with the observational database is about 56%. Also, the average 
                         atmospheric fields reveals that, preferably, more intense cases 
                         are detected by the algorithm. Classification errors occur in both 
                         methodologies, subjective and objective, with can mask the 
                         results. For future studies it is suggested that dynamic and/or 
                         thermodynamic variables are inserted in the algorithm.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59213",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMG5T",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMG5T",
           targetfile = "59213.pdf",
                 type = "Meteorologia e climatologia",
        urlaccessdate = "27 abr. 2024"
}