用AI直接融合多源海洋数据，实现高精度实时状态估计
Advancing Ocean State Estimation with efficient and scalable AI
- 基于神经过程构建连续映射，原生保留观测数据精度
- 从1°粗网格重建0.25°中尺度动态，参数仅增加3.7%
- 与深度学习预报结合，预测能力提升20天，适合实时地球监测
精确且高效的全球海洋状态估计仍是地球系统科学的重大挑战，受限于传统数据同化（DA）与深度学习（DL）方法在计算可扩展性和数据保真度上的双重瓶颈。本文提出一种面向海洋的AI驱动数据同化框架（ADAF-Ocean），可直接融合多源、多尺度观测数据，涵盖稀疏现场测量到4公里分辨率卫星剖面，无需插值或数据稀疏化。受神经过程启发，ADAF-Ocean学习异构输入到海洋状态的连续映射，保持原始数据精度。通过AI超分辨率技术，从1°粗网格重建0.25°中尺度动力学，兼具效率与可扩展性，参数仅比1°配置多3.7%。当与深度学习预报系统耦合时，相比无同化基线，全球预报技能延长最多达20天。该框架为实时、高分辨率地球系统监测提供了计算可行且科学严谨的新路径。
原文摘要 · Abstract (English)
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.
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