Articles | Volume 31, issue 1
https://doi.org/10.5194/npg-31-165-2024
https://doi.org/10.5194/npg-31-165-2024
Research article
 | 
28 Mar 2024
Research article |  | 28 Mar 2024

The sampling method for optimal precursors of El Niño–Southern Oscillation events

Bin Shi and Junjie Ma

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Cited articles

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Short summary
Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.