Author(s): J. K. Kim; M. O. Lee
Linked Author(s):
Keywords: Data assimilation; Numerical ocean models; Observational networks; Inverse methods; Adjoint method; Kalman filter; Optimal interpolation; Direct insertion; Nudging; Optimization
Abstract: The assimilation of measurements into numerical ocean models in order to compute a more accurate forecast is often called data assimilation. The basic concept of data assimilation is to combine real observations via estimation theory with dynamic models. The problem of an ocean data assimilation is to use the observations in such a way that: First, the solution of the model is as close as possible to the observations. Second, the solution of the model is an agreement with the oceanic characteristics. The combining of data and dynamics is a powerful methodology, which makes possible efficient, accurate and realistic estimations which might not otherwise be feasible. It is providing rapid advances in important aspects of both basic ocean science and applied marine technology operations. The specific uses of data assimilation depend upon the relative quality of data sets and models, and the desired purposes of the field and parameter estimates. These uses include the control of errors for state estimates, the estimation of parameters, the elucidation of real ocean dynamical processes, the design of experimental networks, and ocean monitoring and prediction. In this study, the concept and strategy of data assimilation are described and the application examples of data assimilation over the coastal waters of Korea are reviewed. Conclusions and perspectives from this application and description are also presented.
Year: 2003