Published In

Procedia Computer Science

Document Type

Article

Publication Date

6-2016

Abstract

This article presents a framework for performing ensemble and hybrid data assimilation in a weak-constraint four-dimensional variational data assimilation system (w4D-Var). A practical approach is considered that relies on an ensemble of w4D-Var systems solved by the incremental algorithm to obtain flow-dependent estimates to the model error statistics. A proof-of-concept is presented in an idealized context using the Lorenz multi-scale model. A comparative analysis is performed between the weak- and strong-constraint ensemble-based methods. The importance of the weight coefficients assigned to the static and ensemble-based components of the error covariances is also investigated. Our preliminary numerical experiments indicate that an ensemble-based model error covariance specification may significantly improve the quality of the analysis.

Description

Copyright 2016 The Authors. Published by Elsevier B.V.

This is an open access article.

DOI

10.1016/j.procs.2016.05.329

Persistent Identifier

http://archives.pdx.edu/ds/psu/18501

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