Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Gregg A. Jacobs, Brent P. Bartels, Darek J. Bogucki, Francisco J. Beron-Vera, Shuyi S. Chen, Emanuel F. Coelho, Milan Curcic, Annalisa Griffa, Matthew Gough, Brian K. Haus, Angelique C. Haza, Robert W. Helber, Patrick J. Hogan, Helga S. Huntley, Mohamed Iskandarani, Falko Judt, A. D. Kirwan, Nathan Laxague, Arnoldo Valle-Levinson, Bruce L. LipphardtArthur J. Mariano, Hans E. Ngodock, Guillaume Novelli, M. Josefina Olascoaga, Tamay M. Özgökmen, Andrew C. Poje, Ad J.H.M. Reniers, Clark D. Rowley, Edward H. Ryan, Scott R. Smith, Peter L. Spence, Prasad G. Thoppil, Mozheng Wei

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems.

Original languageEnglish (US)
Pages (from-to)98-117
Number of pages20
JournalOcean Modelling
StatePublished - Nov 1 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Oceanography
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science


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