A Sequential Variational Algorithm for Data Assimilation in Oceanography and Meteorology

Abstract

This study theoretically establishes a sequential variational (SVAR) method for the data assimilation in oceanography and meteorology defined on the model space. Requiring a significantly smaller amount of computer memory, theoretically SVAR gives the same optimal model state estimate as the four-dimensional variational data assimilation method. Its computational cost is similar to that of the four-dimensional variational data assimilation and representer methods. In addition to the optimal state estimates, SVAR computes error covariances at the end of the assimilation window. These advantageous properties of the new algorithm are obtained by combining the sequential methodology with suitable definitions of several new l2 norms, which implicitly provide required estimates.