MOHDAS: MaCOM Operational Hybrid Data Assimilation System Based on the Analytical 4-Dimensional Ensemble Variational Method

This study developed the MaCOM Operational Hybrid Data Assimilation System (MOHDAS), which integrates 2 independently developed core technologies. The first is the regional high-resolution Mass Conservation Ocean Model (MaCOM), China’s first self-developed, mass-conserving operational ocean circulation model. The second is an innovative hybrid data assimilation framework that combines the analytical 4-dimensional ensemble variational (A-4DEnVar) method with the 4-dimensional multigrid analysis method. The 4-dimensional multigrid analysis method separates multiscale signals from observational data, thereby providing A-4DEnVar with ensemble members of high signal-to-noise ratio. In addition, a dimensionality-reduction algorithm for A-4DEnVar (DRA-4DEnVar), a preprocessing-based efficient observation projection module, and adaptive workflow control and monitoring scripts are also incorporated into the system. Operational evaluations conducted from 2025 July 1 to September 30 demonstrate that MOHDAS outperforms the state-of-the-art GLO12v4 product from Mercator Ocean in the analysis of temperature/salinity profiles as well as sea surface temperature and successfully captures signals from extreme weather events, such as typhoons, and monthly to subseasonal climate anomalies. MOHDAS provides reliable 1- to 7-d forecasts of temperature/salinity profiles, with higher sea-surface-temperature accuracy than forecasts initialized from the GLO12v4 analysis of Mercator Ocean. The system effectively corrects and maintains systematic biases at a low level throughout the forecast period. Furthermore, the DRA-4DEnVar-based hybrid data assimilation strategy demonstrates promising nonlinear assimilation capability, highlighting the robustness of MOHDAS and its potential for broader applications.