Three-Dimensional Thermohaline Field Forecast Using a Numerical Model Assimilating AI-Reconstructed Parameters in the Western Indian Ocean

Abstract

Accurate three-dimensional temperature and salinity initial fields are essential for regional short-term ocean forecasting, but subsurface constraints remain limited in the Western Indian Ocean because in situ profiles are sparse and satellite observations mainly describe the sea surface. This study evaluated a method to improve short-term forecasts by assimilating pretrained language model (PLM)-reconstructed thermohaline fields into the Finite Volume Community Ocean Model with a three-dimensional variational data assimilation scheme (FVCOM–3DVAR). The method was applied to the Western Indian Ocean, covering 15° S–10° N, 33° E–60° E. A non-assimilation control experiment (Control_run), Modular Ocean Data Assimilation System assimilation (MODAS_ass), and PLM reconstructed-field assimilation (PLM_ass) were conducted to evaluate the forecast performance with 5-day rolling forecasts. World Ocean Database (WOD) profiles were used for profile-based validation, and Copernicus Marine Environment Monitoring Service (CMEMS) gridded fields were used for spatially continuous reference evaluation in February, May, August, and November 2021. The results demonstrated that PLM_ass produced lower temperature and salinity root-mean-square errors (RMSEs) than Control_run and MODAS_ass at most depths and lead times. Relative to Control_run, PLM_ass reduced mean temperature RMSE by 28.0% at 100–200 m and mean salinity RMSE by 23.5% at 0–100 m; compared with MODAS_ass, the reductions were approximately 16.6% and 14.2%, respectively. Spatial diagnostics showed variable-, depth-, and region-dependent impacts with the most robust improvement in thermocline temperature forecasts. This study provides a feasible pathway for incorporating AI-reconstructed subsurface information into regional ocean forecasting systems under sparse observation conditions.

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