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<p>In recent years, Artificial Intelligence (AI)-based weather prediction models have emerged as powerful tools in meteorology, capable of learning complex dependencies from extensive weather datasets and generating rapid forecasts after training. These models achieve prediction accuracies comparable to state-of-the-art Numerical Weather Prediction (NWP) systems. However, these models remain not fully operational due to their dependence on computationally intensive Data Assimilation (DA) systems for generating accurate initial fields. Recent advances in AI techniques offer a potential pathway to develop more efficient and accurate DA systems, advancing the operational feasibility of end-to-end AI-based weather forecasting. Despite growing interest, research in AI-based DA remains fragmented. Therefore, a comprehensive review is necessary to clarify the current progress, identify challenges, and guide the future development of next-generation AI-based DA systems. This review categorizes AI-based DA research into two primary domains. The first domain is AI-empowered DA, where AI enhances individual components such as observation operators, tangent linear and adjoint models, and uncertainty quantification. It also includes latent DA, which helps reduce computational costs. The second domain is AI-based end-to-end DA models, which integrate observations and short-range weather predictions within unified AI frameworks to generate accurate initial fields. We further discuss key challenges and opportunities, including dataset standardization, model evaluation protocols, assimilation of extended observation types, enforcement of physical constraints, and addressing operational scalability. Finally, we emphasize the importance of interdisciplinary collaboration across AI and meteorology in developing practical and reliable AI solutions to enhance DA processes and support more accurate weather forecasting. This review offers practical insights to the research community to expedite the development and operationalization of AI-based DA and end-to-end weather forecasting systems.</p>

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Artificial Intelligence techniques in data assimilation: Emerging approaches, key challenges, and future prospects

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Wuxin Wang,
  • Weicheng Ni,
  • Taikang Yuan,
  • Lilan Huang,
  • Tao Han,
  • Boheng Duan,
  • Xiaoyong Li,
  • Yanlai Zhao,
  • Ben Fei,
  • Lei Bai,
  • Kaijun Ren

摘要

In recent years, Artificial Intelligence (AI)-based weather prediction models have emerged as powerful tools in meteorology, capable of learning complex dependencies from extensive weather datasets and generating rapid forecasts after training. These models achieve prediction accuracies comparable to state-of-the-art Numerical Weather Prediction (NWP) systems. However, these models remain not fully operational due to their dependence on computationally intensive Data Assimilation (DA) systems for generating accurate initial fields. Recent advances in AI techniques offer a potential pathway to develop more efficient and accurate DA systems, advancing the operational feasibility of end-to-end AI-based weather forecasting. Despite growing interest, research in AI-based DA remains fragmented. Therefore, a comprehensive review is necessary to clarify the current progress, identify challenges, and guide the future development of next-generation AI-based DA systems. This review categorizes AI-based DA research into two primary domains. The first domain is AI-empowered DA, where AI enhances individual components such as observation operators, tangent linear and adjoint models, and uncertainty quantification. It also includes latent DA, which helps reduce computational costs. The second domain is AI-based end-to-end DA models, which integrate observations and short-range weather predictions within unified AI frameworks to generate accurate initial fields. We further discuss key challenges and opportunities, including dataset standardization, model evaluation protocols, assimilation of extended observation types, enforcement of physical constraints, and addressing operational scalability. Finally, we emphasize the importance of interdisciplinary collaboration across AI and meteorology in developing practical and reliable AI solutions to enhance DA processes and support more accurate weather forecasting. This review offers practical insights to the research community to expedite the development and operationalization of AI-based DA and end-to-end weather forecasting systems.