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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. 

Abstract Shelf water is influenced by atmospheric forcing, river outflows, and the open ocean. Studying its variability is crucial for understanding anthropogenic impacts on coastal oceans and their transport to the open ocean. In the Middle Atlantic Bight (MAB), the interaction of the Gulf Stream with shelf/slope circulation leads to some of the complex exchanges between the shelf and open ocean along the U.S. East Coast. This study employs a Lagrangian particle tracking approach, grounded in a high‐resolution, data‐assimilative ocean reanalysis, to examine the export pathways of surface shelf water in the MAB. We analyzed over 700 daily images of simulated particle distributions using image clustering techniques. This revealed three distinct export patterns: abrupt entrainment to the Gulf Stream, gradual entrainment, and southern transport. Each pattern was observed roughly equally during the study period from January 2017 to December 2018. The observed export patterns are closely linked to the coastal circulation dynamics near Cape Hatteras. Understanding the timing and duration of these patterns is vital for assessing water quality and predicting the settlement of species that spawn in the region. Our study further underscores the influence of tropical cyclones, including Hurricanes Jose, Maria, and Chris, on these export patterns. These extreme weather events lead to significant shifts in coastal circulation near Cape Hatteras. 

Abstract This study introduces an ensemble learning model for the prediction of significant wave height and average wave period in stations along the U.S. Atlantic coast. The model utilizes the stacking method, combining three base learner models - Lasso regression, support vector machine, and Multi-layer Perceptron - to achieve more precise and robust predictions. To train and evaluate the models, a twenty-year dataset comprising meteorological and wave data was used, enabling forecasts for significant wave height and average wave period at 1, 3, 6, and 12 hour intervals. The data collection involved two NOAA buoy stations situated on the U.S. Atlantic coast. The findings demonstrate that the ensemble learning model constructed through the stacking method yields significantly higher accuracy in predicting significant wave height within the specified time intervals. Moreover, the study investigates the influence of swell waves on forecasting significant wave height and average wave period. Notably, the inclusion of swell waves improves the accuracy of the 12-hour forecast. Consequently, the developed ensemble model effectively estimates both significant wave height and average wave period. The ensemble model outperforms the individual models in forecasting significant wave height and average wave period. This ensemble learning model serves as a viable alternative to conventional coastal models for predicting wave parameters. 

Abstract Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land‐sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF‐Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling.