Comparison of reduced-order, sequential and variational data assimilation methods in the tropical Pacific Ocean

TL;DR

This study compares reduced-order, sequential, and variational data assimilation methods in the Tropical Pacific Ocean, showing that the hybrid approach offers improved results, especially in non-linear regimes with complex dynamics.

Contribution

It introduces a hybrid data assimilation method combining variational and sequential approaches and evaluates its performance in a tropical ocean model.

Findings

Both methods perform similarly in linear regimes.

The hybrid method yields slightly better results overall.

Variational approach better controls model dynamics during non-linear events.

Abstract

This paper presents a comparison of two reduced-order, sequential and variational data assimilation methods: the SEEK filter and the R-4D-Var. A hybridization of the two, combining the variational framework and the sequential evolution of covariance matrices, is also preliminarily investigated and assessed in the same experimental conditions. The comparison is performed using the twin-experiment approach on a model of the Tropical Pacific domain. The assimilated data are simulated temperature profiles at the locations of the TAO/TRITON array moorings. It is shown that, in a quasi-linear regime, both methods produce similarly good results. However the hybrid approach provides slightly better results and thus appears as potentially fruitful. In a more non-linear regime, when Tropical Instability Waves develop, the global nature of the variational approach helps control model dynamics better than the sequential approach of the SEEK filter. This aspect is probably enhanced by the context of the experiments in that there is a limited amount of assimilated data and no model error.