Physically consistent global atmospheric data assimilation with machine learning in latent space.

Area of Science:

• Atmospheric sciences
• Data assimilation
• Machine learning

Background:

• Data assimilation (DA) is crucial for accurate weather forecasting and climate research.
• Traditional DA methods struggle with nonlinear physical constraints, limiting accuracy and robustness.

Purpose of the Study:

• Introduce Latent Data Assimilation (LDA) for improved atmospheric state estimation.
• Develop a framework leveraging autoencoders for Bayesian DA in a latent space.

Main Methods:

• Utilized autoencoders to learn latent representations from multivariate global atmospheric data.
• Performed Bayesian DA within the learned latent space.
• Compared LDA against traditional model-space DA.

Main Results:

• LDA effectively captures nonlinear physical relationships, producing balanced analyses without explicit constraints.
• LDA demonstrated superior analysis quality and forecast skill compared to traditional DA.
• The framework showed robustness across latent dimensions and effectiveness with imperfect training data.

Conclusions:

• LDA offers a flexible and robust alternative to traditional DA methods.
• This approach enhances physical consistency and forecast accuracy in atmospheric modeling.
• LDA shows significant potential for real-world weather forecasting and climate research applications.