Data assimilation (DA) is an essential statistical technique for generating accurate estimates of a physical system's states by combining prior model predictions with observational data, especially in the realm of weather forecasting. Effectively modeling the prior distribution while adapting to diverse observational sources presents significant challenges for both traditional and neural network-based DA algorithms. This paper introduces VAE-Var, a novel neural network-based data assimilation algorithm aimed at 1) enhancing accuracy by capturing the non-Gaussian characteristics of the conditional background distribution $p(\mathbf{x}|\mathbf{x}_b)$, and 2) efficiently assimilating real-world observational data. VAE-Var utilizes a variational autoencoder to learn the background error distribution, with its decoder creating a variational cost function to optimize the analysis states. The advantages of VAE-Var include: 1) it maintains the framework of traditional variational assimilation, enabling it to accommodate various observation operators, particularly irregular observations; 2) it lessens the dependence on expert knowledge for constructing the background distribution, allowing for improved modeling of non-Gaussian structures; and 3) experimental results indicate that, when applied to the FengWu weather forecasting model, VAE-Var outperforms DiffDA and two traditional algorithms (interpolation and 3DVar) in terms of assimilation accuracy in sparse observational contexts, and is capable of assimilating real-world GDAS prepbufr observations over a year.

Atmospheric data assimilation is essential for numerical weather prediction. Ensemble data assimilation connects multiple instances of an atmospheric model through a Kalman filter-based algorithm, which is regarded as a challenging computing task today. In this work, we build a fast, low-cost, and scalable atmospheric data assimilation prototype, DIDA, for the new-generation Sunway supercomputer, including: (1) a framework that enables flexible deployment of components, and manages and optimizes data communication among modules, achieving maximum resource efficiency; (2) an accurate, robust, UNet-based surrogate model for atmospheric dynamic simulation to generate the background ensemble; (3) a batch-LETKF algorithm with high-performance eigenvalue decomposition, which is up to 7.37 times faster than existing numerical libraries while exhibiting almost linear scalability. Experimental evaluations show that our AI-integrated ensemble data assimilation prototype can complete hour-cycle assimilation in minutes, maintain linear scalability, and save an order of magnitude of computing resources, compared with the traditional method.