A new EnOI-based multiscale assimilation system for global ocean circulation model

With advancements in observational techniques and computational technologies, multi-scale data assimilation (MS-DA) has become a significant focus in data assimilation research. Global ocean models with variable resolution grids offer advantages in specific regions, highlighting the need for MS-DA methods tailored for these models. This study develops an MS-DA system that extends the single-scale data assimilation (SS-DA) framework. It integrates a multi-grid interpolation module and uses the MPI-OM ocean model with the Ensemble Kalman Filter (EAKF) assimilation technique. The system aims to enhance computational efficiency and improve the assimilation of temperature and salinity data, particularly in the South China Sea (SCS). The MS-DA system uses a two-step assimilation process to convert high-resolution model data to lower-resolution grids and vice versa, with parallel computing to accelerate data processing. The study compares the computational performance and assimilation outcomes of MS-DA and SS-DA. Results show that the MS-DA system reduces computational time by 20 % compared to SS-DA, with minimal differences in global sea surface temperature (SST) and salinity (SSS) assimilation. However, the MS-DA system performs better in the SCS, reducing the mean absolute error (MAE) for SST and SSS by 0.01 °C and 0.01 PSU, respectively. It also improves temperature and salinity assimilation in the upper 500 m of the SCS by 1.7 % and 6.2 %, respectively. Overall, the MS-DA system offers better assimilation performance with lower computational costs, especially in high-resolution regions like the SCS.