Title

Recent Applications in Representer-Based Variational Data Assimilation

Published In

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications

Document Type

Book Chapter

Publication Date

2013

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 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.

Description

Chapter 12 in the book Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)

DOI

10.1007/978-3-642-35088-7_12

Persistent Identifier

http://archives.pdx.edu/ds/psu/20793

Publisher

Springer