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 DA (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 q

SUBMITTER: Fan H 

PROVIDER: S-EPMC12757030 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature