Author:
Chattopadhyay Ashesh,Gray Michael,Wu Tianning,Lowe Anna B.,He Ruoying
Abstract
AbstractWhile 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.
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Pathak, J. et al. FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. arXiv preprint arXiv:2202.11214 (2022).
2. Lam, R. et al. Graphcast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794 (2022).
3. Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 2023, 1–6 (2023).
4. Dengo, J. The problem of gulf stream separation: A barotropic approach. J. Phys. Oceanogr. 23(10), 2182–2200 (1993).
5. Chassignet, E. & Marshall, D. Gulf Stream Separation in Numerical Ocean Models (American Geophysical Union, 2008).