Improving Indian Ocean Analysis using ROMS with Sea Level Anomaly Assimilation

Regional Analysis of Indian OceaN (RAIN) system, an advanced data assimilation framework,was developed to improve ocean state forecasts in the Indian Ocean. RAIN assimilates in-situ temperature, salinity profiles, and along-track Sea Surface Temperature (SST) using Local Ensemble Transform Kalman Filter (LETKF) blended with Regional Ocean Modeling System (ROMS). The RAIN system has been upgraded in the present study to improve forecast accuracy by incorporating satellite-derived Sea Level Anomaly (SLA), which captures variations in sea surface height caused by oceanic processes like currents, eddies, and mesoscale dynamics. Assimilating SLA in the ROMS is challenging as the model estimated SLA is not characteristically at par with the observed SLA. Steric effects are neglected in the model estimated SLA whereas satellite altimetry measurements include steric height as well. To address this, a steric correction is applied to SLA observations before assimilation to account for the thermal expansion and aligning observed SLA with the ROMS framework. The RAIN system employs a sequential assimilation strategy to integrate corrected SLA alongside in-situ temperature, salinity, and SST to generate a comprehensive ocean state analysis. Assimilation of modified SLA significantly enhances the ability of the RAIN system to forecast ocean states accurately. The upgraded RAIN system enhances the representation of ocean dynamics and mesoscale features, leading to more reliable predictions crucial for marine navigation, climate research, and environmental monitoring in the Indian Ocean region.