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dc.contributor.authorChattopadhyay, Ashesh
Hassanzadeh, Pedram
Subramanian, Devika
dc.date.accessioned 2020-10-16T18:17:13Z
dc.date.available 2020-10-16T18:17:13Z
dc.date.issued 2020
dc.identifier.citation Chattopadhyay, Ashesh, Hassanzadeh, Pedram and Subramanian, Devika. "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." Nonlinear Processes in Geophysics, 27, (2020) Copernicus Publications: 373-389. https://doi.org/10.5194/npg-27-373-2020.
dc.identifier.urihttps://hdl.handle.net/1911/109424
dc.description.abstract In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.
dc.language.iso eng
dc.publisher Copernicus Publications
dc.rights This work is distributed under the Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title 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
dc.type Journal article
dc.citation.journalTitle Nonlinear Processes in Geophysics
dc.citation.volumeNumber 27
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.5194/npg-27-373-2020
dc.type.publication publisher version
dc.citation.firstpage 373
dc.citation.lastpage 389


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