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, Pedram Hassanzadeh, Devika Subramanian

Nonlinear processes in geophysics 2020 article cited by 168

importance 4/10 partially solved Neural Networks and Reservoir Computing climate modeling stage: research

Predicting the short-term evolution and long-term statistics of a multiscale chaotic Lorenz 96 system when only the large-scale variables are observable and the smaller-scale processes are never known.

Unobserved small-scale processes make chaotic multiscale systems fundamentally hard to predict.

Three machine-learning methods (echo state network/reservoir computing, ANN, RNN-LSTM) were trained on only the large-scale X variables to forecast trajectories and reproduce long-term statistics.

RC-ESN substantially outperforms ANN and RNN-LSTM, accurately forecasting chaotic trajectories for hundreds of solver time steps (several Lyapunov timescales); RNN-LSTM beats ANN

Demonstrates reservoir computing as an effective data-driven predictor for chaotic multiscale dynamics from partial observations.

Venue Nonlinear processes in geophysics
Published 2020
Work type article
Authors Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian
Citations 168 (OpenAlex, at import time)
References 85
Model or method RC-ESN (echo state network), ANN, RNN-LSTM
Organization not stated in source
Primary source https://doi.org/10.5194/npg-27-373-2020
Open access gold
Keywords reservoir computing, Lorenz 96, chaotic systems, machine learning, prediction
4 / 10

real but narrower advance

Demonstrates ML prediction of chaotic multiscale dynamics on a toy Lorenz 96 system; abstract only discusses implications for weather/climate, no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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