A reduced-order strategy for 4D-Var data assimilation

TL;DR

This paper introduces a reduced-order 4D-Var data assimilation method that leverages a model-based background error covariance and significantly reduces computational costs, demonstrated through twin experiments in ocean modeling.

Contribution

It proposes a novel reduced-order approach for 4D-Var data assimilation, enhancing efficiency and multivariate error covariance modeling.

Findings

Computational cost decreased by an order of magnitude.

Multivariate background error covariance improves identification.

Method is effective in ocean model twin experiments.

Abstract

This paper presents a reduced-order approach for four-dimensional variational data assimilation, based on a prior EO F analysis of a model trajectory. This method implies two main advantages: a natural model-based definition of a mul tivariate background error covariance matrix $\textbf{B}_r$, and an important decrease of the computational burden o f the method, due to the drastic reduction of the dimension of the control space. % An illustration of the feasibility and the effectiveness of this method is given in the academic framework of twin experiments for a model of the equatorial Pacific ocean. It is shown that the multivariate aspect of $\textbf{B}_r$ brings additional information which substantially improves the identification procedure. Moreover the computational cost can be decreased by one order of magnitude with regard to the full-space 4D-Var method.