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ABSTRACT

Aires and Rossow 2003

Aires, F., and W.B. Rossow, 2003: Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis of feedback processes in a dynamical system: The Lorenz model case study. Q. J. Royal Meteorol. Soc., 129, 239-275, doi:10.1256/qj.01.174.

As an alternative to classical linear feedback analysis, we present a nonlinear approach for the determination of the sensitivities of a dynamical system from observations of its variations. The new methodology consists of statistical estimates of all the pair-wise relationships among the system state variables based on a neural network modeling of the system dynamics (its time evolution). The model can then be used to estimate the instantaneous, multivariate, nonlinear sensitivities. Classical feedback analysis is re-examined in terms of these sensitivities, which are shown to be more fundamental in the analysis of feedback processes than estimates of feedback factors and to provide a more appropriate representation of the system's behavior. The method is described and tested on synthetic observations of the time variations of the Lorenz low-order atmospheric model where the correct sensitivities can be evaluated analytically.

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