基于机器学习的潜空间物理一致全球大气数据同化
Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
AI总结：
本文提出潜空间数据同化方法，通过自动编码器学习非线性物理关系，实现无需显式建模物理约束的高效大气数据同化。
AI中文摘要：
数据同化（DA）将观测与模型预报结合，以产生优化的大气状态，其物理一致性对稳定天气预报和可靠气候研究至关重要。传统贝叶斯DA方法通过经验性和可调的协方差结构强制这些非线性、流动依赖的物理约束，但准确性和鲁棒性有限。本文介绍了一个名为潜空间数据同化（LDA）的框架，该框架通过自动编码器从多变量全球大气数据中学习潜空间，进行贝叶斯DA。我们证明自动编码器能够很大程度上捕捉非线性物理关系，使LDA能够生成平衡分析，而无需显式建模物理约束。在潜空间中的同化相比传统模型空间DA在理想化和真实观测设置下都提高了分析质量和预测技能。此外，LDA在潜空间维度上表现出强鲁棒性，并且即使自动编码器是在不准确但物理上合理的预报上训练的，仍然有效，突显了其在实际应用中的灵活性。
英文摘要：
Data assimilation (DA) integrates observations with model forecasts to produce optimized atmospheric states, whose physical consistency is critical for stable weather forecasting and reliable climate research. Traditional Bayesian DA methods enforce these nonlinear, flow-dependent physical constraints through empirical and tunable covariance structures, but with limited accuracy and robustness. Here, we introduce Latent Data Assimilation (LDA), a framework that performs Bayesian DA in a latent space learned from multivariate global atmospheric data via an autoencoder. We demonstrate that the autoencoder can largely capture nonlinear physical relationships, enabling LDA to produce balanced analyses without explicitly modeling physical constraints. Assimilation in latent space also improves both analysis quality and forecast skill compared to traditional model-space DA, under both idealized and real observational settings. Furthermore, LDA exhibits strong robustness across latent dimensions and remains effective even when the autoencoder is trained on inaccurate but physically realistic forecasts, highlighting its flexibility for real-world applications.
