Hoffman, R. N., Atmospheric and Environmental, Lexington, USA, ross.n.hoffman@aer.com
Blumberg, A. F., Stevens Institute of Technology, Hoboken, USA, Alan.Blumberg@stevens.edu
Ponte, R. M., Atmospheric and Environmental Research, Inc., Lexington, USA, rponte@aer.com
Kostelich, E. J., Dept. of Mathematics and Statistics, Arizona State University, Tempe, USA, kostelich@asu.edu
Szunyogh, I. ., University of Maryland, College Park, USA, szunyogh@ipst.umd.edu
Vinogradov, S. ., Atmospheric and Environmental Research, Inc., Lexington, USA, svinogra@aer.com
Henderson, J. M., Atmospheric and Environmental Research, Inc., Lexington, USA, jhenders@aer.com


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.

Oral presentation

Presentation is given by student: No
Session #:154
Date: 03-03-2008
Time: 14:45

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