Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space

Abstract

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.

References in corpus (6)

• Neural General Circulation Models for Weather and Climate
• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
• Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems
• Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations
• 3D-Var Data Assimilation using a Variational Autoencoder
• FuXi Weather: A data-to-forecast machine learning system for global weather