Data assimilation is the process of hindcasting, now-casting, and forecasting using information from both observations and ocean dynamics. Modern ocean forecasting systems rely on data assimilation to estimate initial and boundary data, to interpolate and smooth sparse or noisy observations, and to evaluate observing systems and dynamical models. Every data assimilation system implements an optimality criterion which defines how to best combine dynamics and observations, given an hypothesized error model for both. The realization of practical ocean data assimilation systems is challenging due to both the technical issues of implementation, and the scientific issues of determining the appropriate set of hypothesized priors. This chapter reviews methodologies and highlights themes common to all approaches.
Available at: http://works.bepress.com/edward_zaron/17/