Incorporation of Dynamic Balance in Data Assimilation and Application to Coastal Oceans

Zhijin Li
Jet Propulsion Laboratory, NASA

Kayo Ide
Institute of Geophsyics and Planetary Physics, University of California, Los Angeles

Data assimilation in meteorology and oceanography is commonly described as the process through which all the observed and predicted information are used in order to estimate as accurately as possible the state of atmospheric or oceanic flow and the algorithm is rooted in optimal estimation theory. However, the estimated state should be constrained to be close to or on slow manifolds or dynamic attractors, and current data assimilation algorithms do not incorporate this capability in the framework of optimal estimation theory. We are exploring a theoretical framework to address this issue and suggesting a practical method.

Last Update: September 29, 2005