Advancing Ocean State Estimation with efficient and scalable AI

TL;DR

This paper introduces ADAF-Ocean, an AI-based data assimilation framework that efficiently integrates diverse ocean observations, enhances resolution, and extends forecast skill, advancing real-time ocean monitoring.

Contribution

The paper presents ADAF-Ocean, a novel AI-driven data assimilation framework that directly assimilates multi-source data and achieves high-resolution ocean state estimation with improved scalability.

Findings

Reconstructs mesoscale dynamics from coarse data with minimal additional parameters.

Extends global forecast skill by up to 20 days.

Operates efficiently with scalable AI-driven super-resolution.

Abstract

Accurate and efficient global ocean state estimation remains a grand challenge for Earth system science, hindered by the dual bottlenecks of computational scalability and degraded data fidelity in traditional data assimilation (DA) and deep learning (DL) approaches. Here we present an AI-driven Data Assimilation Framework for Ocean (ADAF-Ocean) that directly assimilates multi-source and multi-scale observations, ranging from sparse in-situ measurements to 4 km satellite swaths, without any interpolation or data thinning. Inspired by Neural Processes, ADAF-Ocean learns a continuous mapping from heterogeneous inputs to ocean states, preserving native data fidelity. Through AI-driven super-resolution, it reconstructs 0.25$^\circ$ mesoscale dynamics from coarse 1$^\circ$ fields, which ensures both efficiency and scalability, with just 3.7\% more parameters than the 1$^\circ$ configuration. When coupled with a DL forecasting system, ADAF-Ocean extends global forecast skill by up to 20 days compared to baselines without assimilation. This framework establishes a computationally viable and scientifically rigorous pathway toward real-time, high-resolution Earth system monitoring.