Accession Number:

AD1005754

Title:

Ocean Current Estimation Using a Multi-Model Ensemble Kalman Filter During the Grand Lagrangian Deployment Experiment (GLAD)

Descriptive Note:

OSTP Journal Article

Corporate Author:

NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS STENNIS SPACE CENTER United States

Report Date:

2014-12-27

Pagination or Media Count:

23.0

Abstract:

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 drifters 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 andor 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.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE