An accurate estimation of Lagrangian transport in the ocean is important for a number of practical problems such as dispersion of pollutants, biological species, and sediments. Forecasting of the Lagrangian pathways necessarily relies on the accuracy of ocean and coastal models. However, these models include a number of errors that propagate directly from the Eulerian velocity field to the Lagrangian transport. In this study, so-called Lagrangian sub-grid-scale, or LSGS, model is developed to reduce errors projected to Lagrangian transport from errors arising from missing physics, uncertainties in forcing and unresolved scales in OGCMs. The LSGS method acts on the diagnostics of particle transport computed from coastal or ocean models, and it allows to minimize the discrepancy between the statistical behavior of the modeled (synthetic) and real (observed) trajectories. The method is shown to work well using both a so-called Markov velocity field model, representing an idealized turbulent flow field, and in the context of the Navy Coastal Ocean Model (NCOM) configured in the Adriatic Sea for realistic, high-resolution, complex ocean flows. The simplicity and computational efficiency of this technique, combined with applicability to ocean models at a wide range of resolutions, appears promising in light of the challenge of capturing exactly the oceanic turbulent fields, which is critical for Lagrangian dispersion.
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Geotechnical Engineering and Engineering Geology
- Atmospheric Science