Lagrangian Data Assimilation for Improving Model Estimates of Velocity Fields and Residual Currents in a Tidal Estuary

Numerical models are associated with uncertainties that can be reduced through data assimilation (DA). Lower costs have driven a recent tendency to use Lagrangian instruments such as drifters and floats to obtain information about water bodies. However, difficulties emerge in their assimilation, since Lagrangian data are set out in a moving frame of reference and are not compatible with the fixed grid locations used in models to predict flow variables. We applied a pseudo-Lagrangian approach using OpenDA, an open-source DA tool to assimilate Lagrangian drifter data into an estuarine hydrodynamic model. Despite inherent challenges with using drifter datasets, the work showed that low-cost, low-resolution drifters can provide a relatively higher improvement over the Eulerian dataset due to the larger area coverage of the drifter. We showed that the assimilation of Lagrangian data obtained from GPS-tracked drifters in a tidal channel for a few hours can significantly improve modelled velocity fields (up to 30% herein). A 40% improvement in residual current direction was obtained when assimilating both Lagrangian and Eulerian data. We conclude that the best results are achieved when both Lagrangian and Eulerian datasets are assimilated into the hydrodynamic model.

Multi-Platform, High-Resolution Study of a Complex Coastal System: The TOSCA Experiment in the Gulf of Trieste

Although small in size, the Gulf of Trieste (GoT), a marginal coastal basin in the northern Adriatic Sea, is characterized by very complex dynamics and strong variability of its oceanographic conditions. In April–May 2012, a persistent, large-scale anticyclonic eddy was observed in the GoT. This event was captured by both High Frequency Radar (HFR) and Lagrangian drifter observations collected within the European MED TOSCA (Tracking Oil Spill and Coastal Awareness) project. The complexity of the system and the variety of forcing factors constitute major challenges from a numerical modeling perspective when it comes to simulating the observed features. In this study, we implemented a high-resolution hydrodynamic model in an attempt to reproduce and analyze the observed basin-wide eddy structure and determine its drivers. We adopted the Massachusetts Institute of Technology General Circulation Model (MITgcm), tailored for the GoT, nested into a large-scale simulation of the Adriatic Sea and driven by a tidal model, measured river freshwater discharge data and surface atmospheric forcing. Numerical results were qualitatively and quantitatively evaluated against HFR surface current maps, Lagrangian drifter trajectories and thermohaline data, showing good skills in reproducing the general circulation, but failing in accurately tracking the drifters. Model sensitivity to different forcing factors (wind, river and tides) was also assessed.

Improving the Accuracy of Hydrodynamic Model Predictions Using Lagrangian Calibration

While significant studies have been conducted in Intermittently Closed and Open Lakes and Lagoons (ICOLLs), very few have employed Lagrangian drifters. With recent attention on the use of GPS-tracked Lagrangian drifters to study the hydrodynamics of estuaries, there is a need to assess the potential for calibrating models using Lagrangian drifter data. Here, we calibrated and validated a hydrodynamic model in Currimundi Lake, Australia using both Eulerian and Lagrangian velocity field measurements in an open entrance condition. The results showed that there was a higher level of correlation (R2 = 0.94) between model output and observed velocity data for the Eulerian calibration compared to that of Lagrangian calibration (R2 = 0.56). This lack of correlation between model and Lagrangian data is a result of apparent difficulties in the use of Lagrangian data in Eulerian (fixed-mesh) hydrodynamic models. Furthermore, Eulerian and Lagrangian devices systematically observe different spatio-temporal scales in the flow with larger variability in the Lagrangian data. Despite these, the results show that Lagrangian calibration resulted in optimum Manning coefficients (n = 0.023) equivalent to those observed through Eulerian calibration. Therefore, Lagrangian data has the potential to be used in hydrodynamic model calibration in such aquatic systems.

Lagrangian Drifter to Identify Ocean Eddy Characteristics

Deterministic–stochastic empirical mode decomposition (EMD) is used to obtain low-frequency (non-diffusive; i.e., background velocity) and high-frequency (diffusive; i.e., eddies) components from a Lagrangian drifter‘s trajectory. Eddy characteristics are determined from the time series of eddy trajectories from individual Lagrangian drifters such as eddy radius, eddy velocity, eddy Rossby number, and the eddy–current kinetic energy ratio. A long-term dataset of the Sound Fixing and Ranging (RAFOS) float time series obtained near the California coast by the Naval Postgraduate School from 1992 to 2004 at depth between 150 and 600 m is used as an example to demonstrate the capability of the deterministic–stochastic EMD.

Lagrangian Drifter to Identify Ocean Eddy Characteristics

Deterministic-stochastic empirical mode decomposition (EMD) is used to obtain low-frequency (non-diffusive, i.e., background velocity) and high-frequency (diffusive, i.e., eddies) components from a Lagrangian drifter‘s trajectory. Eddy characteristics are determined from the time series of eddy trajectories from individual Lagrangian drifter such as the eddy radial scale, eddy velocity scale, eddy Rossby number, and eddy-background kinetic energy ratio. A long-term dataset of the SOund Fixing And Ranging float time series obtained near the California coast by the Naval Postgraduate School from 1992 to 2004 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) is used as an example to demonstrate the capability of the deterministic-stochastic EMD.