4D-Var Data Assimilation of Sea Surface Temperature in a Regional Model of the Andaman Sea

Andaman Sea (AnS) is a marginal sea in the eastern Bay of Bengal (BoB) and the least studied in Indian Ocean, making it challenging to simulate. Using data assimilation with satellite sea surface temperature observations is a feasible approach to obtaining high-quality simulations. Our goal is to develop such a system for the AnS. First, we perform a high-resolution data-driven numerical Regional Ocean Modeling System (ROMS) control run. Second, a data-assimilative ROMS run with MODIS SST observations is performed and compared to the control run. Initial and boundary conditions are taken from the Nucleus for European Modelling of the Ocean (NEMO) model for both the control and data assimilation runs. We use an Incremental Strong Constraint 4-Dimensional Variational (I4DVar) data assimilation scheme. This is the first study to implement the time-evolving data assimilation ocean model in the AnS region at such a high resolution and gives a good estimate of the ocean state in AnS. The data assimilation run significantly reduces the difference between the AnS simulations and the BD12 observations over the control run. Significantly, the data assimilation run reduces the surface Temperature error by 0.5°C when compared with the control run, when MODIS SST data is assimilated. This data assimilation set-up is essential to improve our ability to understand the synoptic dynamics of the region better.