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Abstract While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for regional sea-suface height emulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill compared to a state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate initial steps for physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.
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This content will become publicly available on July 1, 2026
Ocean Emulation With Fourier Neural Operators: Double Gyre
Abstract A data‐driven emulator for the baroclinic double gyre ocean simulation is presented in this study. Traditional numerical simulations using partial differential equations (PDEs) often require substantial computational resources, hindering real‐time applications and inhibiting model scalability. This study presents a novel approach employing Fourier neural operators to address these challenges in an idealized double‐gyre ocean simulation. We propose a deep learning approach capable of learning the underlying dynamics of the ocean system, complementing the classical methods. Additionally, we show how Fourier neural operators allow us to train the network at one resolution and generate ensembles at a different resolution. We find that there is an intermediate time scale where the prediction skill is maximized.
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- Award ID(s):
- 2103942
- PAR ID:
- 10635541
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 17
- Issue:
- 7
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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