Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
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New preprint by Ross et al.
In this work, led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Laure Zanna, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations.
See code and notebooks here: https://github.com/m2lines/pyqg_parameterization_benchmarks