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@InProceedings{CintraCampCock:2016:MuPeDa,
               author = "Cintra, Rosangela Saher Correa 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}",
                title = "Multilayer perceptron on data assimilation applied to FSU global 
                         model",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "International Symposium on Uncertainty Quantification and 
                         Stochastic Modeling, 3. (Uncertainties)",
             keywords = "data assimilation, artificial neural networks, ensemble Kalman 
                         filter, multilayer perceptron.",
             abstract = "Numerical weather prediction (NWP) uses atmospheric general 
                         circulation models (AGCMs) to predict weather based on current 
                         weather conditions. The atmosphere could not be completely 
                         described due to inherent uncertainty. These uncertainties limit 
                         forecast model accuracy to about five or six days into the future. 
                         The process of entering observation data into mathematical model 
                         to generate the accurate initial conditions is called data 
                         assimilation (DA). This paper shows the results of a DA technique 
                         using artificial neural networks (NN) applied to an AGCM used in 
                         Florida State University (FSU) in USA. The Local Ensemble 
                         Transform Kalman filter (LETKF), a version of Kalman filter with 
                         ensembles to represent the model uncertainties, is a traditional 
                         DA scheme. We use Multilayer Perceptron data assimilation (MLP-DA) 
                         with supervised training algorithm where NN receives input vectors 
                         with their corresponding response from LETKF initial conditions. 
                         These DA schemes are applied to FSU Global Spectral Model 
                         (FSUGSM), a multilevel spectral primitive equation model at 
                         resolution T63L27. This data assimilation experiment is based in 
                         synthetic observations: surface pressure and upper-air 
                         temperature. We use a NN self-configuration method to find the 
                         optimal NN parameters to configure the MLP-DA with: four input 
                         vector nodes and one output node for the analysis vector. The NNs 
                         were trained with data from each month of 2001, 2002, and 2003. 
                         The MLP-DA cycle is performed for January 2004. The numerical 
                         results demonstrate the effectiveness of the MLP-DA technique for 
                         atmospheric data assimilation, since the initial conditions have 
                         similar quality to LETKF. The reduced computational cost allows 
                         the inclusion of greater number of observations and new data 
                         sources and the use of high resolution of models.",
  conference-location = "Maresias, SP",
      conference-year = "15-19 Feb.",
        urlaccessdate = "23 nov. 2020"
}


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