An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm

Abstract EN

Data assimilation is an efficient technique in the estimation of ocean state, by introducing the benefit of in situ measurements.

Considering the insufficiency of the observations, the performance of assimilation with few temperature and salinity (T-S) profiles is not satisfied.

To modify the situation, an extending algorithm based on the Argo temperature profile is developed and applied to present more reconstructed information.

Meanwhile, when the reconstructed information is assimilated into the ocean model, the accuracy of the outcomes would obtain a notable enhancement.

To validate it, an experiment including four cases is conducted based on Regional Ocean Modeling System (ROMS) and 4-dimensional variational method (4DVAR).

The comparison with the EN4 dataset shows that the cases assimilated the Argo and the reconstructed temperature profiles are both promoted; the addition of reconstructed temperature profiles does enhance the accuracy; the impact of SST introduced in the extending algorithm process is negligible; the net enhancement of reconstructed temperature profiles is comparable with Argo T-S observations.

Finally, the positive impact of the developed algorithm on data assimilation is validated.