Ashesh Chattopadhyay
- Pedram Hassanzadeh
- Devika Subramanian
bibliography:
- 'template.bib'
title: 'Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM'
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Introduction
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Various components of the Earth system involve multi-scale, multi-physics processes and high-dimensional chaotic dynamics. These processes are often modeled using sets of strongly-coupled nonlinear partial differential equations (PDEs), which are solved numerically on supercomputers. As we aim to simulate such systems with increasing levels of fidelity, we need to increase the numerical resolutions and/or incorporate more physical processes from a wide range of spatio-temporal scales into the models. For example, in atmospheric modeling for predicting the weather and climate systems, we need to account for the nonlinear interactions across the scales of cloud microphysics processes, gravity waves, convection, baroclinic waves, synoptic eddies, and large-scale circulation, just to name a few, not to mention the fast/slow processes involved in feedbacks from the ocean, land, and cryosphere [@collins2006formulation; @collins2011development; @flato2011earth; @bauer2015quiet; @jeevanjee2017perspective].

Solving the coupled systems of PDEs representing such multi-scale processes is computationally prohibitive for many practical applications. As a result, to make the simulations tractable, a few strategies have been developed, which mainly involve only solving for 