Articles | Volume 24, issue 1
Nonlin. Processes Geophys., 24, 101–112, 2017
Nonlin. Processes Geophys., 24, 101–112, 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.

Related subject area

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

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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.