@InCollection{CintraCamp:2018:DaAsAr,
               author = "Cintra, Rosangela Saher Corr{\^e}a and Campos Velho, Haroldo 
                         Fraga de",
                title = "Data assimilation by artificial neural networks for an atmospheric 
                         general circulation model",
            booktitle = "Advanced applications for artificial neural Networks",
            publisher = "Intech",
                 year = "2018",
               editor = "El-Shahat, A.",
                pages = "265--285",
              address = "Janeza Trdine (Rijeka) Croatia",
             keywords = "Artificial neural networks, Data assilimation, Numerical weather 
                         prediction, Computer performance, Ensemble Kalman filter.",
             abstract = "Numerical weather prediction (NWP) uses atmospheric general 
                         circulation models (AGCMs) to predict weather based on current 
                         weather conditions. The process of entering observation data into 
                         mathematical model to generate the accurate initial conditions is 
                         called data assimilation (DA). It combines observations, 
                         forecasting, and filtering step. This paper presents an approach 
                         for employing artificial neural networks (NNs) to emulate the 
                         local ensemble transform Kalman filter (LETKF) as a method of data 
                         assimilation. This assimilation experiment tests the Simplified 
                         Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an 
                         atmospheric general circulation model (AGCM), using synthetic 
                         observational data simulating localizations of meteorological 
                         balloons. For the data assimilation scheme, the supervised NN, the 
                         multilayer perceptrons (MLPs) networks are applied. After the 
                         training process, the method, forehead-calling MLP-DA, is seen as 
                         a function of data assimilation. The NNs were trained with data 
                         from first 3 months of 1982, 1983, and 1984. The experiment is 
                         performed for January 1985, one data assimilation cycle using 
                         MLP-DA with synthetic observations. The numerical results 
                         demonstrate the effectiveness of the NN technique for atmospheric 
                         data assimilation. The results of the NN analyses are very close 
                         to the results from the LETKF analyses, the differences of the 
                         monthly average of absolute temperature analyses are of order 102. 
                         The simulations show that the major advantage of using the MLP-DA 
                         is better computational performance, since the analyses have 
                         similar quality. The CPU-time cycle assimilation with MLP-DA 
                         analyses is 90 times faster than LETKF cycle assimilation with the 
                         mean analyses used to run the forecast experiment.",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                 isbn = "9789535137801",
                label = "lattes: 5142426481528206 2 CintraCamp:2018:DaAsAr",
             language = "en",
           targetfile = "cintra_data.pdf",
                  url = "https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/data-assimilation-by-artificial-neural-networks-for-an-atmospheric-general-circulation-model",
        urlaccessdate = "27 abr. 2024"
}