An Ensemble Kalman Filtering Approach for Regional Ocean Data Assimilation

Current and future operational needs of the Navy require the capability to combine very large and disparate datasets with dynamical ocean numerical models to produce, in real time, accurate analyses and forecasts together with respective uncertainty estimates for any littoral region of the globe. We propose to implement a highly scalable, portable and efficient ocean data assimilation system based on (i) the Estuarine and Coastal Ocean Model (ECOM), a state-of-the-art primitive equation model that has been applied to many studies of the coastal zone, and (ii) a recent adaptation of ensemble Kalman filtering techniques, developed at the University of Maryland, that works particularly well for very large non-linear dynamical systems, in both sparse and dense data regimes, and that provides efficient algorithms for error estimation and quality control. Under Phase II of the project, extensive development of the prototype system developed in Phase I will be pursued to evaluate the skill of analyses and forecasts under different flow regimes and boundary conditions, diverse data streams, and various model errors. Our ultimate goal is to develop and transition the system to Navy operations and apply it to satisfy the diverse ocean modeling and data assimilation needs of many other potential users in the commercial and public sectors.