@InProceedings{CamposVelho:2021:NeNeNe,
               author = "Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Neural Network as a New Method for Data Assimilation",
            booktitle = "Proceedings...",
                 year = "2021",
         organization = "Mathematical Congress of the Americas (MCA)",
             abstract = "Data assimilation is an essential issue for all operational 
                         centers with focus on numerical weather prediction, hydrology, 
                         ocean circulation, environmental forecasting, space weather, and 
                         ionospheric dynamics. Several methods has been proposed for data 
                         assimilation based on Kalman filter, variational schemes, and 
                         particle filter. However, such strategies has very high 
                         computational effort. Our investigation is to apply a 
                         self-configuring supervised artificial neural network to address 
                         the data assimilation process, with significant reduction of the 
                         CPU-time. Results will be shown for different models: shallow 
                         water 2D for ocean circulation simulation, global spectral 3D 
                         meteorological models (SPEED, and COAPS-FSU), and a regional 
                         meteorological model (WRF-NCAR).",
  conference-location = "Online",
      conference-year = "19-23 July",
             language = "en",
           targetfile = "S20-02123-FragadeCamposVelho.pdf",
        urlaccessdate = "21 maio 2024"
}