@Article{HärterCamp:2010:MuPeNe,
               author = "H{\"a}rter, Fabr{\'{\i}}cio Pereira and Campos Velho, Haroldo 
                         Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Multlayer perceptron neural network in a data assimilation 
                         scenario",
              journal = "Engineering Applications of Computational Fluid Mechanics",
                 year = "2010",
               volume = "4",
               number = "2",
                pages = "237--245",
             keywords = "Data assimilation, Extended Kalman filter, Artificial neural 
                         network, Multi-layer perceptron, Dynamo atmospheric model.",
             abstract = "Multilayer Perceptron Neural Network (MLP-NN) have been 
                         successfully applied to solve nonlinear problems in meteorology 
                         and oceanography. In this work, MLP-NN is applied to completely 
                         emulate an Extended Kalman Filter (EKF) in a data assimilation 
                         scenario. Data assimilation is a process for producing a good 
                         combination of data from observations and data from a mathematical 
                         model. This is a fundamental issue in an operational prediction 
                         system. The one-dimensional shallow water equation DYNAMO-1D is 
                         employed here for testing the assimilation schemes. The DYNAMO 
                         model is derived from depth-integrating the Navier-Stokes 
                         equations, in the case where the horizontal length scale is much 
                         greater than the vertical length scale, where the Coriolis force 
                         is also considered in atmospheric flows. Techniques, such as 
                         Extend Kalman Filter, are available to track non-linear dynamical 
                         models under certain conditions. Under strong non-linearity, the 
                         fourth-order moment EKF works well when applied to high 
                         dimensional state space for data assimilation, but the 
                         computational burden is a barrier in this kind of application. 
                         Artificial Neural Network (ANN) is an alternative solution for 
                         this computational complexity problem, once the ANN is trained 
                         offline with a high order Kalman filter, even though this Kalman 
                         filter has high computational cost (which is not a problem during 
                         ANN training phase). The results achieved in this research 
                         encourage us to apply this technique on operational models. 
                         However, it is not yet possible to assure convergence in high 
                         dimensional problems.",
                  doi = "10.1080/19942060.2010.11015313",
                  url = "http://dx.doi.org/10.1080/19942060.2010.11015313",
                 issn = "1994-2060",
                label = "lattes: 5142426481528206 2 H{\"a}rterCamp:2010:MuPeNe",
             language = "en",
        urlaccessdate = "29 jun. 2024"
}