An ensemble Kalman filtering approach for regional ocean data assimilation

Author: Ross N. Hoffman, Rui M. Ponte, E.J. Kostelich, A.F. Blumberg, I. Szunyogh, Sergey Vinogradov and John M. Henderson
Date: 
July 2, 2007
Type: 
Presentation
Venue: 
XXIV IUGG General Assembly, Perugia, Italy
Citation: 

Hoffman, R.N., R.M. Ponte, E. Kostelich, A. Blumberg, I. Szunyogh, S. Vinogradov, and J.M. Henderson, 2007. An ensemble Kalman filtering approach for regional ocean data assimilation, XXIV IUGG General Assembly, Perugia, Italy, July 2007.

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 I of the project, basic feasibility tests of the data assimilation system will be carried out for the northern Gulf of Mexico, using simulated data for simplicity and evaluating the skill of the analyses and forecasts under different assumed noise levels and flow regimes, with the ultimate goal of developing and transitioning the system to Navy operations and applying it to satisfy the diverse ocean modeling and data assimilation needs of many other potential users in the commercial and public sectors.