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Q. J. R. Meteorol. Soc. (2003), 129, pp. 239275 doi: 10.1256/qj.01.174 Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis
 

Summary: Q. J. R. Meteorol. Soc. (2003), 129, pp. 239­275 doi: 10.1256/qj.01.174
Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis
of feedback processes in a dynamical system: Lorenz model case-study
By FILIPE AIRES1;2¤
and WILLIAM B. ROSSOW3
1Columbia University, NASA Goddard Institute for Space Studies, USA
2
Laboratoire de M´et´eorologie Dynamique du CNRS, France
3NASA Goddard Institute for Space Studies, USA
(Received 17 October 2001; revised 17 May 2002)
SUMMARY
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
modelling 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 behaviour. 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.

  

Source: Aires, Filipe - Laboratoire de Météorologie Dynamique du CNRS, Université Pierre-et-Marie-Curie, Paris 6
Fridlind, Ann - Earth Science Division, NASA Ames Research Center

 

Collections: Environmental Sciences and Ecology; Geosciences