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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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Pedram Hassanzadeh on behalf of the Authors (01 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (22 May 2020) by Zoltan Toth
RR by Istvan Szunyogh (24 May 2020)
RR by Anonymous Referee #2 (26 May 2020)
ED: Publish subject to technical corrections (27 May 2020) by Zoltan Toth
AR by Pedram Hassanzadeh on behalf of the Authors (28 May 2020)  Author's response    Manuscript
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Short summary
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.