  Data assimilation (DA), as an indispensable component within contemporary
Numerical Weather Prediction (NWP) systems, plays a crucial role in generating
the analysis that significantly impacts forecast performance. Nevertheless, the
development of an efficient DA system poses significant challenges,
particularly in establishing intricate relationships between the background
data and the vast amount of multi-source observation data within limited time
windows in operational settings. To address these challenges, researchers
design complex pre-processing methods for each observation type, leveraging
approximate modeling and the power of super-computing clusters to expedite
solutions. The emergence of deep learning (DL) models has been a game-changer,
offering unified multi-modal modeling, enhanced nonlinear representation
capabilities, and superior parallelization. These advantages have spurred
efforts to integrate DL models into various domains of weather modeling.
Remarkably, DL models have shown promise in matching, even surpassing, the
forecast accuracy of leading operational NWP models worldwide. This success
motivates the exploration of DL-based DA frameworks tailored for weather
forecasting models. In this study, we introduces FuxiDA, a generalized DL-based
DA framework for assimilating satellite observations. By assimilating data from
Advanced Geosynchronous Radiation Imager (AGRI) aboard Fengyun-4B, FuXi-DA
consistently mitigates analysis errors and significantly improves forecast
performance. Furthermore, through a series of single-observation experiments,
Fuxi-DA has been validated against established atmospheric physics,
demonstrating its consistency and reliability.
