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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 24, 101-112, 2017
https://doi.org/10.5194/npg-24-101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
22 Feb 2017
Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems
Qin Zheng et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Comment on "Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems"', Anonymous Referee #1, 21 Dec 2016 Printer-friendly Version 
RC2: 'review of the paper "Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems"', Anonymous Referee #2, 21 Dec 2016 Printer-friendly Version 
AC2: 'Comments and Reply of Reviewer #2', Zubin Yang, 18 Jan 2017 Printer-friendly Version Supplement 
AC1: 'Comments and Reply of Reviewer #1', Zubin Yang, 18 Jan 2017 Printer-friendly Version Supplement 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zubin Yang on behalf of the Authors (18 Jan 2017)  Author's response  Manuscript
ED: Publish as is (01 Feb 2017) by Jinqiao Duan  
CC BY 4.0
Publications Copernicus
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Short summary
When the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model on the prediction variable will lead to failure of the ADJ-CNOP method; when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making false estimations of the lower bound of maximum predictable time.
When the initial perturbation is large or the prediction time is long, the strong nonlinearity...
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