Data Driven Reconstruction of Upper Ocean Profiles for Improved State Estimation in the Philippine Sea

Despite significant advances in observational systems such as the global Argo array of autonomous profiling floats, the spatiotemporal coverage of subsurface ocean observations remains limited compared to the dense data provided by satellite platforms. This study develops a data-driven framework to reconstruct synthetic profiles of upper ocean temperature and salinity by training a self-attention-based neural network with satellite-derived sea surface height (SSH) and sea surface temperature anomalies, using 17 years of collocated Argo float measurements. Daily synthetic profiles for the upper 650 m of the Philippine Sea were generated for the entirety of 2010 and assimilated into a regional ocean model via 4D-Var data assimilation. Results show overall improved effectiveness of state estimation when synthetic profiles are utilized. Diagnostic variables like temperature, salinity, SSH, horizontal velocity all show improvement. Synthetic profiles of subsurface temperature had an overall positive impact on SSH analysis and forecast, especially in regions east of the Luzon Strait and the southern domain influenced by the North Equatorial Current. In the vertical range of 150–600 m, the impact of synthetic profiles on various observations was promising, leading to substantial reductions in the analysis and forecast error.