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Eulerian velocity fields are derived from 300 drifters released in the Gulf of Mexico by The Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment. These data are directly assimilated into the Navy Coastal Ocean Model (NCOM) four-dimensional variational data assimilation (4DVAR) analysis system in a series of experiments to investigate their impact on the model circulation. The NCOM-4DVAR is a newly developed tool for data analysis, formulated for weak-constraint data assimilation based on the indirect representer method. The assimilation experiments take advantage of this velocity data along with other available data sources from in situ and satellite measurements of surface and subsurface temperature and salinity. Three different experiments are done: (i) A nonassimilative NCOM free run, (ii) an assimilative NCOM run that utilizes temperature and salinity observations, and (iii) an assimilative NCOM run that uses temperature and salinity observations as well as the GLAD velocity observations. The resulting analyses and subsequent forecasts are compared to assimilated and future GLAD velocity and temperature/salinity observations to determine the performance of each experiment and the impact of the GLAD data on the analysis and the forecast. It is shown that the NCOM-4DVAR is able to fit the observations not only in the analysis step, but also in the subsequent forecast. It is also found that the GLAD velocity data greatly improves the characterization of the circulation, with the forecast showing a better fit to future GLAD observations than those experiments without the velocity data included.

The Lagrangian predictability of general circulation models is limited by the need for high-resolution data streams to constrain small-scale dynamical features. Here velocity observations from Lagrangian drifters deployed in the Gulf of Mexico during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment are assimilated into the Naval Coastal Ocean Model (NCOM) 4D variational (4DVAR) analysis system to examine their impact on Lagrangian predictability. NCOM-4DVAR is a weak-constraint assimilation system using the indirect representer method. Velocities derived from drifter trajectories, as well as satellite and in situ observations, are assimilated. Lagrangian forecast skill is assessed using separation distance and angular differences between simulated and observed trajectory positions. Results show that assimilating drifter velocities substantially improves the model forecast shape and position of a Loop Current ring. These gains in mesoscale Eulerian forecast skill also improve Lagrangian forecasts, reducing the growth rate of separation distances between observed and simulated drifters by approximately 7.3 km day 21 on average, when compared with forecasts that assimilate only temperature and salinity observations. Trajectory angular differences are also reduced.FIG. 9. (a) Mean separation distance (km) after 24 h between the GLAD and simulated drifters for each assimilation cycle. The average is taken only over the drifters removed in the data-denial experiment (cf. Fig. 4). (b) The minimum distance of each unassimilated drifter to the nearest assimilated drifter, averaged over all the unassimilated drifters at each hourly time step.

The assimilation of surface velocity observations and their impact on the model sea surface height (SSH) is examined using an operational regional ocean model and its four-dimensional variational data assimilation (4DVAR) analysis component. In this work, drifter-derived surface velocity observations are assimilated into the Navy’s Coastal Ocean Model (NCOM) 4DVAR in weak-constraint mode for a Gulf of Mexico (GoM) experiment during August–September 2012. During this period the model is trained by assimilating surface velocity observations (in a series of 96-h assimilation windows), which is followed by a 30-day forecast through the month of October 2012. A free-run model and a model that assimilates along-track SSH observations are also run as baseline experiments to which the other experiments are compared. It is shown here that the assimilation of surface velocity measurements has a substantial impact on improving the model representation of the forecast SSH on par with the experiment that assimilates along-track SSH observations directly. Finally, an assimilation experiment is done where both along-track SSH and velocity observations are utilized in an attempt to determine if the observation types are redundant or complementary. It is found that the combination of observations provides the best SSH forecast, in terms of the fit to observations, when compared to the previous experiments.

Progressive vector diagrams (PVDs) have been used to estimate transport in the coastal ocean from point measurements of velocity time series, although they strictly only approximate true particle trajectories in regions where the currents are spatially uniform. Currents in most coastal regions vary significantly in both space and time, making coastal transport estimates from PVDs questionable. Here, we used synoptic surface currents measured over periods of several months by HF radars in two coastal areas with distinctly different circulation features (the Gulf of Eilat, a deep, semi-enclosed basin, and the Delaware Bay mouth, a coastal estuarine outflow) to assess the time scales over which PVD paths computed from velocity time series at fixed points separate from particle trajectories computed from two-dimensional measured horizontal currents that vary in both space and time. In both study regions, PVDs and particle trajectories separate by 1 km over a mean time period of 7-10 h with no significant month-to-month variation in either region. The separation time statistics presented here should serve as a strong caution for investigators motivated to estimate transport using only point measurements.