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Hybrid Methods in Engineering

ISSN Druckformat: 1099-2391
ISSN Online: 2641-7359

Archives: Volume 1, 1999 to Volume 4, 2002

Hybrid Methods in Engineering

DOI: 10.1615/HybMethEng.v2.i3.50
20 pages

DATA ASSIMILATION USING AN ADAPTIVE KALMAN FILTER AND LAPLACE TRANSFORM

A. G. Nowosad
Instituto Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515 12201-970, Sao Jose dos Campos (SP), Brazil
A. Rios Neto
Instituto Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515 12201-970, Sao Jose dos Campos (SP), Brazil
Haroldo F. de Campos Velho
Institute Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515, 12201-970 São José dos Campos - SP, Brazil

ABSTRAKT

An Adaptive Extended Kalman Filter is used for data assimilation in two nonlinear dynamical systems: the Lorenz system in chaotic state and the computational model DYNAMO for the atmosphere. This approach does not require the modeling error to be stationary and uses a Linear Kalman Filter to estimate this error. This method is compared to the methods using Laplace transform, and Linear and Extended Kalman Filters. The conclusion was that the choice between using Laplace transform and Adaptive Kalman Filter assimilation methods for DYNAMO depended on whether one was willing to completely reject high-frequency information or not. When that information was considered useless, the Laplace filtering eliminated it better than the Kalman filtering. Otherwise, Kalman assimilated it better than Laplace.


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