Articles | Volume 24, issue 1
Nonlin. Processes Geophys., 24, 101–112, 2017
https://doi.org/10.5194/npg-24-101-2017
Nonlin. Processes Geophys., 24, 101–112, 2017
https://doi.org/10.5194/npg-24-101-2017

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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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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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.
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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.