Symbiotic Ocean Modeling Using Physics-Controlled Echo State Networks

Abstract

We introduce a “symbiotic” ocean modeling strategy that leverages data-driven and machine learning methods to allow high- and low-resolution dynamical models to mutually benefit from each other. In this work we mainly focus on how a low-resolution model can be enhanced within a symbiotic model configuration. The broader aim is to enhance the representation of unresolved processes in low-resolution models, while simultaneously improving the efficiency of high-resolution models. To achieve this, we use a grid-switching approach together with hybrid modeling techniques that combine linear regression-based methods with nonlinear echo state networks. The approach is applied to both the Kuramoto–Sivashinsky equation and a single-layer quasi-geostrophic ocean model, and shown to simulate short-term and long-term behavior better than either purely data-based methods or low-resolution models. By maintaining key flow characteristics, the hybrid modeling techniques are also able to provide higher quality initial conditions for high-resolution models, thereby improving their efficiency. Key Points We propose a symbiotic ocean modeling framework in which models of different complexities benefit from each other Unresolved processes are represented through hybrid machine learning methods using data from the symbiotic framework Hybrid correction strategies with imperfect physics as control input improve the representation of key long-term flow properties