Articles | Volume 27, issue 3
Nonlin. Processes Geophys., 27, 373–389, 2020
https://doi.org/10.5194/npg-27-373-2020
Nonlin. Processes Geophys., 27, 373–389, 2020
https://doi.org/10.5194/npg-27-373-2020
Research article
02 Jul 2020
Research article | 02 Jul 2020

Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

Ashesh Chattopadhyay et al.

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Cited articles

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The performance of three machine-learning methods for data-driven modeling of a multiscale chaotic Lorenz 96 system is examined. One of the methods is found to be able to predict the future evolution of the chaotic system well from just knowing the past observations of the large-scale component of the multiscale state vector. Potential applications to data-driven and data-assisted surrogate modeling of complex dynamical systems such as weather and climate are discussed.