Do Assimilated Drifter Velocities Improve Lagrangian Predictability in an Operational Ocean Model?

Abstract

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.

Impact of data assimilation on ocean current forecasts in the Angola Basin

“…Drifter velocity assimilation is shown on average to significantly improve Lagrangian predictability of 4 different metrics in line with previous studies [37,35,9]. OSCAR assimilation only improves Lagrangian predictability as much as altimetry and only by half as much as the drifter assimilation.…”

“…A variety of skill metrics were used (see Table 2) to allow for robust conclusions. This is in agreement with previous work in the Gulf of Mexico [35,9] and Mediterranean [37].…”

“…hours [35], and a normalized 3 day cumulative distance difference skill score i.e. the ratio of the cumulative separation between drifters and simulated floats and cumulative distance travelled by the observed drifter after 3 days [25].…”

“…Recent research on the assimilation of drifter velocities remains focussed on the Gulf of Mexico (GOM) due to a large amount of drifter data available. Muscarella et al [35] and Carrier et al [9] utilised the Grand Lagrangian Deployment (GLAD) data set (300 drifters released in a short period in a localised region in the GOM) and have shown that assimilating drifter inferred velocities improves both the Lagrangian predictability and sea level forecast. Berta et al [3] has combined GLAD drifters and altimetry in a Lagrangian -variational scheme to produce instantaneous estimates of ocean current velocities improving the hind-cast trajectory skill.…”

“…Our approach was similar to that of Muscarella et al [35] but differed in the following ways: Firstly, we sought to separately quantify the individual and combined use of altimetry, OSCAR and drifters. This was the first time OSCAR has been assimilated using an advanced data assimilation method such as 4D-Var.…”