@Article{CamposVelhoFSBWSCC:2022:PaIm,
               author = "Campos Velho, Haroldo Fraga de and Furtado, Helaine Cristina 
                         Morais and Sambatti, Sabrina B{\'e}rgoch Monteiro and Barros, 
                         Carla Osthoff Ferreira de and Welter, Maria Eugenia Sausen and 
                         Souto, Roberto Pinto and Carvalho, Diego and Cardoso, Douglas O.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Oeste do Par{\'a} (UFOPA)} and {} and 
                         {Laborat{\'o}rio Nacional de Computa{\c{c}}{\~a}o 
                         Cient{\'{\i}}fica (LNCC)} and {Laborat{\'o}rio Nacional de 
                         Computa{\c{c}}{\~a}o Cient{\'{\i}}fica (LNCC)} and 
                         {Laborat{\'o}rio Nacional de Computa{\c{c}}{\~a}o 
                         Cient{\'{\i}}fica (LNCC)} and {Centro Federal de 
                         Educa{\c{c}}{\~a}o Tecnol{\'o}gica Celso Suckow da Fonseca} and 
                         {Centro Federal de Educa{\c{c}}{\~a}o Tecnol{\'o}gica Celso 
                         Suckow da Fonseca}",
                title = "Data Assimilation by Neural Network for Ocean Circulation: 
                         Parallel Implementation",
              journal = "Supercomputing Frontiers and Innovations",
                 year = "2022",
               volume = "9",
               number = "1",
                pages = "74--86",
             keywords = "Data assimilation, Articial Neural Network, Shallow water 
                         equations, Parallel processing.",
             abstract = "Data assimilation (DA) is an essential issue for operational 
                         prediction centers, where a com-puter code is applied to simulate 
                         physical phenomena by solving differential equations. The 
                         pro-cedure to determine the best initial condition combining data 
                         from observation and previousforecasting (background) is carried 
                         out by a data assimilation method. The Kalman filter (KF) isa 
                         technique for data assimilation, but it is computationally 
                         expensive. An approach to reduce thecomputational effort for DA is 
                         to emulate the KF by a neural network. The multi-layer 
                         perceptronneural network (MLP-NN) is employed to emulate the 
                         Kalman in a 2D ocean circulation model,and algorithmic complexity 
                         to KF and NN is presented. A shallow-water system models the 
                         oceandynamics. Synthetic measurements are used for evaluating the 
                         MLP-NN for the data assimilationprocess. Here, a parallel version 
                         for the DA procedure by the neural network is described andtested, 
                         showing the performance improvement for a parallel version of the 
                         NN-DA.",
                  doi = "10.14529/jsfi220105",
                  url = "http://dx.doi.org/10.14529/jsfi220105",
                 issn = "2409-6008",
                label = "lattes: 5142426481528206 1 CamposVelhoFSBWSCC:2022:PaIm",
             language = "en",
           targetfile = "superfri-2022-1-74-86.pdf",
                  url = "https://superfri.org/index.php/superfri/issue/view/33",
        urlaccessdate = "10 maio 2024"
}