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.

翻译：资料同化（DA）作为当代数值天气预报（NWP）系统中不可或缺的组成部分，在生成显著影响预报性能的分析场中起着关键作用。然而，开发高效的DA系统面临重大挑战，特别是在业务运行中有限的时间窗口内建立背景场与海量多源观测数据之间复杂关系方面。为应对这些挑战，研究人员针对每种观测类型设计复杂的预处理方法，利用近似建模和超级计算集群的能力来加速求解。深度学习（DL）模型的出现带来了革命性变革，提供了统一的多模态建模、增强的非线性表征能力以及卓越的并行化性能。这些优势推动了将DL模型整合到天气建模各个领域的努力。值得注意的是，DL模型在匹配甚至超越全球领先业务NWP系统预报精度方面展现出巨大潜力。这一成功促使我们探索适用于天气预报模型的基于DL的DA框架。在本研究中，我们提出Fuxi-DA，一个用于同化卫星观测的广义基于DL的DA框架。通过同化风云四号B星搭载的先进地球同步辐射成像仪（AGRI）数据，FuXi-DA持续减小分析误差并显著提升预报性能。此外，通过一系列单观测试验，Fuxi-DA已根据既定大气物理学得到验证，证明了其一致性和可靠性。