Reduced-order 4D-Var: a preconditioner for the Incremental 4D-Var data assimilation method

TL;DR

This paper introduces a reduced-order preconditioning approach for incremental 4D-Var data assimilation, significantly speeding up convergence and reducing computational cost in oceanographic applications without sacrificing accuracy.

Contribution

It proposes a novel reduced-order 4D-Var preconditioning technique that enhances the efficiency of incremental 4D-Var in ocean data assimilation.

Findings

Cost reduced by a factor of 2

Feasible and efficient in tropical Pacific Ocean

Maintains solution quality

Abstract

This study demonstrates how the incremental 4D-Var data assimilation method can be applied efficiently preconditione d in an application to an oceanographic problem. The approach consists in performing a few iterations of the reduced-order 4D-Var prior to the incremental 4D-Var in the full space in order to achieve faster convergence. An application performed in the tropical Pacific Ocean, with assimilation of TAO temperature data, shows the method to be both feasible and efficient. It allows the global cost of the assimilation to be reduced by a factor of 2 without affecting the quality of the solution.