Advancing Ocean State Estimation with efficient and scalable AI

Real-time, high-fidelity ocean state estimation is a prerequisite for Earth system digital twins, yet faces a dilemma between the computational bottlenecks of traditional assimilation and the grid-based fidelity losses of deep learning. Here we present ADAF-Ocean, a geometry-agnostic framework that resolves this by assimilating multi-source observations directly at their original resolutions. Leveraging a neural process-based architecture, our approach learns a continuous mapping from heterogeneous inputs, such as sparse profiles and satellite imagery, thereby maximizing information extraction while enforcing multivariate physical consistency. Although purely data-driven, ADAF-Ocean is capable of implicitly learning the coupling patterns between thermodynamic and kinematic variables directly from high-fidelity datasets. Evaluations show that superior analysis accuracy gives rise to emergent physical coherence.  Serving as superior initial conditions for a DL forecast model, these coherent fields sustain a significant forecast skill advantage for up to 20 days. Furthermore, by quantifying the contribution of individual observational sources, this framework establishes a trustworthy pathway for AI-driven oceanography, bridging data-driven efficiency with the rigorous standards of Earth system monitoring.