%0 Journal Article
%4 dpi.inpe.br/plutao@80/2008/12.04.13.27.58
%2 dpi.inpe.br/plutao@80/2008/12.04.13.27.59
%@doi 10.1016/j.apm.2007.09.006
%@issn 0307-904X
%F lattes: 5142426481528206 2 HärterCamp:2008:NeApAp
%T New approach to applying neural network in nonlinear dynamic model
%D 2008
%A Härter, Fabrício Pereira,
%A Campos Velho, Haroldo Fraga de,
%@affiliation
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress fabricio.harter@inmet.gov.br
%@electronicmailaddress haroldo@lac.inpe.br
%B Applied Mathematical Modelling
%V 32
%N 12
%P 2621-2633
%K dynamo model, data assimilation, extended Kalman filter, artificial neural network, radial base function.
%X In this work, radial basis function neural network (RBF-NN) is applied to emulate an extended Kalman filter (EKF) in a data assimilation scenario. The dynamical model studied here is based on the one-dimensional shallow water equation DYNAMO-1D. This code is simple when compared with an operational primitive equation models for numerical weather prediction. Although simple, the DYNAMO-1D is rich for representing some atmospheric motions, such as Rossby and gravity waves. It has been shown in the literature that the ability of the EKF to track nonlinear models depends on the frequency and accuracy of the observations and model errors. In some cases, just fourth-order moment EKF works well, but will be unwieldy when applied to high-dimensional state space. 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 work encourage us to apply this technique on operational model. However, it is not yet possible to assure convergence in high dimensional problems.
%@language en
%3 1-s2.0-S0307904X07002296-main.pdf