Design and initial tests of a coastal ocean data assimilation system

Date: March 02, 2008 - March 07, 2008

Type: Presentation

Venue: AGU Ocean Sciences Meeting, Orlando


Hoffman, R.N., A.F. Blumberg, R.M. Ponte, E.J. Kostelich, I. Szunyogh, S.V. Vinogradov, and J.M. Henderson, 2008. Design and initial tests of a coastal ocean data assimilation system, AGU Ocean Sciences Meeting, Orlando, March 2008.

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A coastal ocean data assimilation system is being developed. The goal is to combine large and disparate datasets with ocean numerical models, producing accurate analyses, forecasts, and respective uncertainty estimates for any littoral region. A modular interface combines the Estuarine and Coastal Ocean Model (ECOM) and the Local Ensemble Transform Kalman Filter (LETKF) into a highly scalable, portable and efficient ocean data assimilation system. LETKF, a recent adaptation of ensemble Kalman filtering techniques, works particularly well for very large non-linear dynamical systems in both sparse and dense data regimes, and provides efficient algorithms for error estimation and quality control. In simulation experiments involving the New York Harbor Observing and Prediction System (NYHOPS) the filter quickly converges, eliminating bias and greatly reducing rms errors. This behavior is robust to changes in ensemble size, data coverage, and data error. Future goals include applying the system to real data in operational environments and evaluating the skill of analyses and forecasts under different flow regimes and boundary conditions, with diverse data streams, and in various model configurations.