Efficient data-driven flow modeling for accurate passive scalar advection in submesoscale domains

Knowing the sea surface velocity field is important for various applications, such as search and rescue operations, where predicting the movement of objects or substances is critical. However, achieving an accurate estimation of these advection processes is challenging, even with modern measuring equipment, such as high-frequency radar or advanced simulations based on oceanic flow models. Therefore, this paper presents a data-driven framework to approximate sea surface velocity from spatially distributed observations, thus enabling efficient probability advection modeling across submesoscale domains. The system uses quasi-steady flow assumptions to approximate transient flows. To overcome the limitations of point measurements in capturing domain-wide circulation, the method employs a fusion of two simplified 2D flow models to approximate submesoscale dynamics, enabling complete velocity field reconstruction from scattered data. To ensure reliable flow dynamics, the approach iteratively adjusts boundary conditions in numerical simulations to align the simulated flow with observations. Experimental validation was conducted in Kvarner Bay using Global Positioning System (GPS) drifters. The results confirmed the system’s ability to replace computationally intensive transient simulations by approximating flow fields using model simplifications. The results demonstrate its efficiency in various cases, making it a practical tool for real-life submesoscale applications requiring swift passive scalar advection.