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.
@article{arxiv.2408.11438,
title = {Benchmarking AI-based data assimilation to advance data-driven global weather forecasting},
author = {Wuxin Wang and Weicheng Ni and Ben Fei and Tao Han and Lilan Huang and Taikang Yuan and Xiaoyong Li and Lei Bai and Boheng Duan and Kaijun Ren},
journal= {arXiv preprint arXiv:2408.11438},
year = {2026}
}