Recent Applications in Representer-Based Variational Data Assimilation
Data assimilation with representer-based algorithms (also called “dual space” algorithms) are currently being used for weak-constraint four-dimensional variational data assimilation (W4D-Var) atmospheric prediction, distributed parameter estimation, and other hydrodynamic data assimilation problems. The iterative linear solvers at the core of these systems may display non-monotonic convergence in the norm defined by the primal objective function, and this behavior makes problematic the development of practical stopping criteria. One approach to this problem is described, namely an implementation of the inner solver using the generalized conjugate residual(GCR) algorithm. Additional elements of data assimilation systems are error model for the background, model forcings, and observations. An implementation of a posterior analysis method for diagnosing the error variances is described, and representative results from an atmospheric data assimilation systems are shown.
Zaron was sponsored by the National Science Foundation (NSF), award OCE-0623540, with additional support from the Naval Research Laboratory, award N00173-08-2-C015. Authors Chua, Xu, Baker, and Rosmond gratefully acknowledge the support of their sponsors, the Naval Research Laboratory, the Office of Naval Research, and the PMW-120, under program elements, 0602435N and 0603207N, respectively. Computational resources for Zaron were provided by the National Center for Atmospheric Research, which is sponsored by NSF.
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