Symbiotic Ocean Modeling Using Physics‐Controlled Echo State Networks.

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. Plain Language Summary: Models of the ocean vary in complexity. Some are very detailed and manage to show oceanic vortices, whereas others are very efficient but coarse, and unable to compute such vortices. The idea in this paper is to let these different model types work together and benefit from each other, as if in a symbiosis. With knowledge of differences between the detailed and coarse model we can use machine learning techniques to improve the coarse model. In this way a coarse model can be used to provide good quality predictions and to aid a detailed model by taking over part of its computations. We apply our ideas to the Kuramoto–Sivashinsky (KS) model and a quasi‐geostrophic (QG) ocean model, where we show that promising short‐term KS results may generalize to models of the ocean. Long‐term equilibrium experiments with QG show in addition how the correction strategies let a coarse model produce correct flow properties, where standalone physics‐ or data‐based approaches fail. These improved coarse models are computationally cheap, yet good enough to give initial conditions for the fine model, showcasing the symbiotic modeling idea. Key Points: We propose a symbiotic ocean modeling framework in which models of different complexities benefit from each otherUnresolved processes are represented through hybrid machine learning methods using data from the symbiotic frameworkHybrid correction strategies with imperfect physics as control input improve the representation of key long‐term flow properties [ABSTRACT FROM AUTHOR]

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