用深度学习重构缺失的海温非线性演变，即使观测数据仅10%也保持高精度。
Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework
- 基于生成式AI的隐空间数据融合框架，捕捉海温非线性变化。
- 观测仅10%时误差增长不超过40%，仍稳定生成非线性演化。
- 揭示模型对多尺度海洋信号的物理可解释性，适合气候建模与数据稀疏场景。
数据同化（DA）方法的进步显著提升了地球系统预测精度。为融合多源数据并重建观测中缺失的非线性演变，地球科学家正发展面向未来的数据同化方法。本文重新设计了一种纯数据驱动的隐空间数据同化框架（DeepDA），采用生成式人工智能模型捕捉海表温度的非线性演变。在变分约束下，嵌入非线性特征的DeepDA能有效融合异构数据。结果表明，即使观测信息大量缺失，DeepDA仍能保持高度稳定，准确捕捉并生成非线性演变。当仅提供10%观测信息时，其误差增长不超过40%。此外，DeepDA在真实观测与集合模拟数据融合中表现稳健。本文从物理模式角度分析了DeepDA生成的非线性演变机制，揭示了该深度学习模型在捕捉多尺度海洋信号方面的内在可解释性。
原文摘要 · Abstract (English)
Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.
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