A Revised Scheme to Compute Horizontal Covariances in an Oceanographic 3D-VAR Assimilation System

TL;DR

This paper introduces a third-order recursive filter to improve the efficiency and accuracy of horizontal covariance calculations in an oceanographic 3D-VAR data assimilation system, significantly reducing computational time.

Contribution

The paper presents a novel third-order recursive filter for horizontal covariance approximation in 3D-VAR, enhancing computational efficiency and accuracy over traditional first-order methods.

Findings

Reduced computational time in data assimilation processes.

Improved accuracy of Gaussian covariance modeling.

Demonstrated suitability for operational oceanographic applications.

Abstract

We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of a RF with first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An advantage of the proposed scheme is that the CPU's time can be substantially reduced with benefits on the large scale applications. Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications. The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation.