Recent Applications in Representer-Based Variational Data Assimilation - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 281

Chapter 12
Recent Applications in Representer-Based
Variational Data Assimilation

Boon S. Chua, Edward D. Zaron, Liang Xu, Nancy L. Baker,
and Tom Rosmond

Abstract 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 param-
eter 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 gener-
alized 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.