XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledge

• URL: http://arxiv.org/abs/2507.09202v1
• Date: Sat, 12 Jul 2025 08:46:58 GMT
• Title: XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledge
• Authors: Wuxin Wang, Weicheng Ni, Lilan Huang, Tao Hao, Ben Fei, Shuo Ma, Taikang Yuan, Yanlai Zhao, Kefeng Deng, Xiaoyong Li, Boheng Duan, Lei Bai, Kaijun Ren,
• Abstract summary: XiChen is the first observation-scalable fully AI-driven global weather forecasting system.<n>XiChen's entire pipeline, from Data Assimilation (DA) to medium-range forecasting, can be accomplished within only 17 seconds.
• Score: 7.915120865599803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in Artificial Intelligence (AI) demonstrate significant potential to revolutionize weather forecasting. However, most AI-driven models rely on Numerical Weather Prediction (NWP) systems for initial condition preparation, which often consumes hours on supercomputers. Here we introduce XiChen, the first observation-scalable fully AI-driven global weather forecasting system, whose entire pipeline, from Data Assimilation (DA) to medium-range forecasting, can be accomplished within only 17 seconds. XiChen is built upon a foundation model that is pre-trained for weather forecasting. Meanwhile, this model is subsequently fine-tuned to serve as both observation operators and DA models, thereby scalably assimilating conventional and raw satellite observations. Furthermore, the integration of four-dimensional variational knowledge ensures that XiChen's DA and medium-range forecasting accuracy rivals that of operational NWP systems, amazingly achieving a skillful forecasting lead time exceeding 8.25 days. These findings demonstrate that XiChen holds strong potential toward fully AI-driven weather forecasting independent of NWP systems.

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