Intercomparison of high resolution ocean reanalysis products with observations, for exploring the spatiotemporal characteristics in the Indian Ocean

The Indian Ocean plays a crucial role in the global climate and marine ecosystems. Understanding its dynamics is essential for climate predictions, disaster preparedness, and resources management. Ocean reanalysis products offer valuable insights into these dynamics, offering a comprehensive representation of the ocean state over extended periods. However, the accuracy and reliability of these reanalysis products vary across different spatial and temporal domains depending on the model configurations and data assimilation techniques used. This study aims to inter-compare high-resolution ocean reanalysis products, namely GLORYS12V1 and Global Ocean Ensemble Reanalysis, with in situ and satellite observations to assess their performances in capturing spatiotemporal features of the Indian Ocean. Machine learning techniques will be employed to extract meaningful patterns and relationships from the vast oceanographic data, enhancing our understanding of the dynamics of the region. The study will focus on key oceanographic variables, including temperature, salinity, sea surface temperature (SST), sea surface salinity (SSS), and ocean currents. In situ observations from Conductivity Temperature Depth (CTD) profiles, buoys, and satellites will serve as the reference data. Overall the study will contribute to a comprehensive understanding of identifying regions and parameters where the reanalysis products exhibit significant biases or discrepancies compared to observations, Enabling researchers to refine model parameterization schemes, improve data assimilation techniques, and develop more accurate and reliable ocean models.