Istituto di Scienze Marine     
Chang Y., Hammond D., Hazaa A., Hogan P., Huntley H., Kirwan Jr. A., Lipphardt Jr. B., Taillandier V., Griffa A. Enhanced estimation of sonobuoy trajectories by velocity reconstruction with near-surface drifters. In: Ocean Modelling, vol. 36 (3-4) pp. 179 - 197. Elsevier Ltd, 2010.
An investigation to improve trajectory prediction using Lagrangian data is presented. The velocity field of a data assimilating model, EAS-16, is corrected using drifter observations taken during an experiment off Taiwan. The results are tested using another independent Lagrangian data set provided by sonobuoys launched in the same area. The latter have instrument chains that extend well into the water column. Consequently the corrected model velocities were projected into the water column in order to calculate sonobuoy trajectories. The drifter and sonobuoy trajectories both show two distinct regimes in the considered area of approximately 1/2 square. One regime is dominated by shelf dynamics, the other by meandering of the Kuroshio, with a sharp boundary dividing the two. These two regimes are not reproduced by the trajectories of the EAS-16 model. When the drifter data are blended with the model velocities, synthetic sonobuoy trajectories track the observed ones much better, and the two regimes are clearly depicted. Two different methods for the velocity reconstruction are tested. One is based on a variational approach and the other on a normal mode decomposition. Both methods show qualitatively similar improvements in the prediction of sonobuoys trajectories, with a quantitative improvement in the total rms error of approximately 50% and 25%, respectively.
DOI: 10.1016/j.ocemod.2010.11.002
Subject Drifter trajectories
Lagrangian analysis
Lagrangian data assimilation
Lagrangian current measurement

Icona documento 1) Download Document PDF

Icona documento Open access Icona documento Restricted Icona documento Private


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional