@InProceedings{CintraCampCock:2017:SuNeNe,
               author = "Cintra, Rosangela Saher and Campos Velho, Haroldo Fraga de and 
                         Cocke, Steven",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Florida State 
                         University (FSU)}",
                title = "Supervised neural network for data assimilation on atmospheric 
                         general circulation model",
                 year = "2017",
         organization = "International WMO Symposium on Data Assimilation, 7.",
             abstract = "Data assimilation (DA) is an essential process for the operational 
                         prediction centers, due to uncertainties associated to the 
                         forecasting model. Supervised artificial neural network (NN) is 
                         the DA method applied to an Atmospheric General Circulation Model 
                         (AGCM) used in Florida State University (FSU), USA. The NN is 
                         trained to have similar performance to the Local Ensemble 
                         Transform Kalman Filter (LETKF). The NN is self-configured, as a 
                         result of minimizing an optimization problem. There are three 
                         factors in the cost function: training error, generalization 
                         error, and NN complexity. The optimum solution for the NN 
                         configuration is found by using a new meta-heurisc named MCPA 
                         (Multi-Particle Collision Algorithm). The DA experiment was 
                         carried out on the FSU Global Spectral Model (FSUGSM), a 
                         multilevel spectral primitive equation model at resolution T63L27. 
                         Similar results for DA are obtained by NN and LETKF, but the NN 
                         scheme is dozens times faster than the ensemble method.",
  conference-location = "Florian{\'o}polis, SC",
      conference-year = "11-15 Sept.",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}