Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

Emanuel F. Coelho, P. Hogan, G. Jacobs, P. Thoppil, H. S. Huntley, B. K. Haus, B. L. Lipphardt, A. D. Kirwan, E. H. Ryan, J. Olascoaga, F. Beron-Vera, A. C. Poje, A. Griffa, T. M. Özgökmen, A. J. Mariano, G. Novelli, A. C. Haza, D. Bogucki, S. S. Chen, M. CurcicM. Iskandarani, F. Judt, N. Laxague, A. J.H.M. Reniers, A. Valle-Levinson, M. Wei

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3. km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4. km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter's observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.

Original languageEnglish (US)
Pages (from-to)86-106
Number of pages21
JournalOcean Modelling
StatePublished - Mar 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

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


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