Research on Artificial Intelligence (AI)-based Data Assimilation (DA) is expanding rapidly. However, the absence of an objective, comprehensive, and real-world benchmark hinders the fair comparison of diverse methods. Here, we introduce DABench, a benchmark designed for contributing to the development and evaluation of AI-based DA methods. By integrating real-world observations, DABench provides an objective and fair platform for validating long-term closed-loop DA cycles, supporting both deterministic and ensemble configurations. Furthermore, we assess the efficacy of AI-based DA in generating initial conditions for the advanced AI-based weather forecasting model to produce accurate medium-range global weather forecasting. Our dual-validation, utilizing both reanalysis data and independent radiosonde observations, demonstrates that AI-based DA achieves performance competitive with state-of-the-art AI-driven four-dimensional variational frameworks across both global weather DA and medium-range forecasting metrics. We invite the research community to utilize DABench to accelerate the advancement of AI-based DA for global weather forecasting.

翻译：基于人工智能（AI）的数据同化（DA）研究正在迅速扩展。然而，由于缺乏客观、全面且基于真实场景的基准，阻碍了多种方法的公平比较。本文介绍了DABench，这是一个专为促进基于AI的DA方法开发与评估而设计的基准。通过整合真实观测数据，DABench为验证长期闭环DA循环提供了一个客观、公平的平台，同时支持确定性和集合配置。此外，我们评估了基于AI的DA在为先进的AI天气预报模型生成初始条件方面的效能，以产生准确的中期全球天气预报。我们利用再分析数据和独立的无线电探空仪观测进行的双重验证表明，基于AI的DA在全球天气DA和中期预报指标上均达到了与最先进的AI驱动四维变分框架相竞争的性能。我们邀请研究界利用DABench来加速基于AI的DA在全球天气预报领域的进步。