Abstract
Circulation in the Gulf of Mexico is dominated by the Loop Current and associated mesoscale eddies. These mesoscale eddies pose a safety risk to offshore energy production and potential dispersal of large-scale pollutants like oil. We use a data-driven, physics-informed and numerically consistent deep learning-based ocean emulator called OceanNet to generate a 120-day forecast of the SSH in the eastern Gulf of Mexico. OceanNet uses a new dataset of high-resolution data assimilative ocean reanalysis (1993-2022) as input. This model is trained using years 1993-2018 and evaluated on four eddies during years 2019-2021. For comparison, we use a state-of-the-art numerical ocean model to generate a dynamical model prediction initialized every 5 days from April 27, 2019 to April 1, 2020 (during eddies Sverdrup and Thor) using persistent forcing and boundary conditions. The dynamical model takes 7 wall-clock days to run whereas OceanNet runs in minutes. Edges of Loop Current Eddies (LCEs) pose the most potent risk to offshore energy operations and pollutant dispersal due to strong water velocities. Therefore, most of the analysis focuses on edge accuracy, quantified by the Modified Hausdorff Distance. The edge of the LCEs is defined by the 17 cm sea surface height contour, which generally coincides with the strongest water velocity. The OceanNet prediction outperforms both persistence and the dynamical model prediction. Overall, this new ocean emulator provides a promising new approach to generate seasonal forecasts of LCEs and generates large model ensembles efficiently to quantify forecast uncertainty that is long-needed by scientists and decision-makers for offshore operations.
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