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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 1
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.
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

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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Cited articles

Banks, A., Vincent, J., and Anyakoha, C.: A review of particle swarm optimization. Part I: background and development, Nat. Comput., 6, 467–484, https://doi.org/10.1007/s11047-007-9049-5, 2007.
Banks, A., Vincent, J., and Anyakoha, C.: A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications, Nat. Comput., 7, 109–124, https://doi.org/10.1007/s11047-007-9050-z, 2008.
Birgin, E. G., Martinez, J. M., and Raydan, M.: Nonmonotone spectral projected gradient methods on convex sets, SIAM J. Optimiz., 10, 1196–1211, 2000.
Borges, M. D and Hartmann, D. L.: Barotropic Instability and Optimal Perturbations of Observed Nonzonal Flows, J. Atmos. Sci., 49, 335–353, 1992.
Buizza, R. and Palmer, T. N.: The Singular-Vector Structure of the Atmospheric Global Circulation, J. Atmos. Sci., 52, 1434–1456, 1995.
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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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