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Simulation and Data Assimilation in a Chaotic Dynamical System by Cellular Neural Networks

Author

Listed:
  • C. M. O. Oliveir Jr.

    (University of Sao Paulo – USP)

  • A. M. Saraiva

    (University of Sao Paulo – USP)

  • A. C. B. Delbem

    (University of Sao Paulo – USP)

  • F. P. Härter

    (Federal University of Pelotas – UFPel)

  • G. G. Z. Lemos

    (National Institute for Space Research – INPE)

  • H. F. Campos Velho

    (National Institute for Space Research – INPE)

Abstract

Weather and climate predictions are very important topics because of their impact on several activities of society. One essential feature of a prediction system is to compute the best initial condition for starting a forecasting cycle. The procedure to identify the best initial condition is a method by combining observation data from a dynamical system with data from a previous prediction, and this process is called data assimilation (DA). Some non-linear time evolution differential equations present dynamics very sensitive to any tiny changes of initial conditions, exhibiting a chaotic dynamic. Hence, our experiments applying Cell-NN are performed by using the classical Lorenz chaotic model. The methodology is described, where the Cell-NN is presented, then the Lorenz model is shown, and methods for data assimilation (variational and Cell-NN) are explained. The configuration of algorithms is introduced and results for numerical experiments are shown.

Suggested Citation

  • C. M. O. Oliveir Jr. & A. M. Saraiva & A. C. B. Delbem & F. P. Härter & G. G. Z. Lemos & H. F. Campos Velho, 2026. "Simulation and Data Assimilation in a Chaotic Dynamical System by Cellular Neural Networks," Springer Books,, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-04458-7_17
    DOI: 10.1007/978-3-032-04458-7_17
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