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A comparison of data-driven approaches to build low-dimensional ocean models
[article]
2021
arXiv
pre-print
We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The reference dataset is provided by an idealized, eddy-resolving, double-gyre ocean circulation model. Our goal is to conduct a systematic and comprehensive assessment and comparison of skill, cost, and complexity of statistical models from the three methodological classes. The model based on LR is considered as a
arXiv:2108.00818v1
fatcat:bdxc5h2hirg4rcdl4oyuyul2ru