HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis

• URL: http://arxiv.org/abs/2603.01121v1
• Date: Sun, 01 Mar 2026 14:13:11 GMT
• Title: HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis
• Authors: Shuo Tang, Jiadong Zhang, Jian Xu, Gengxian Zhou, Qizhao Jin, Qinxuan Wang, Yi Hu, Ning Hu, Hongchang Ren, Lingli He, Jiaolan Fu, Jingtao Ding, Shiming Xiang, Chenglin Liu,
• Abstract summary: HVR-Met is a meteorological diagnostic system characterized by the deep integration of expert knowledge.<n>Its central innovation is the Hypothesis-Verification-Replanning'' closed-loop mechanism.<n>We introduce a novel benchmark focused on atomic-level subtasks.
• Score: 45.18017161777437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and expert-level prior judgment. Although agents possess inherent advantages in task decomposition and autonomous execution, current architectures are still hampered by critical bottlenecks: inadequate expert knowledge integration, a lack of professional-grade iterative reasoning loops, and the absence of fine-grained validation and evaluation systems for complex workflows under extreme conditions. To this end, we propose HVR-Met, a multi-agent meteorological diagnostic system characterized by the deep integration of expert knowledge. Its central innovation is the ``Hypothesis-Verification-Replanning'' closed-loop mechanism, which facilitates sophisticated iterative reasoning for anomalous meteorological signals during extreme weather events. To bridge gaps within existing evaluation frameworks, we further introduce a novel benchmark focused on atomic-level subtasks. Experimental evidence demonstrates that the system excels in complex diagnostic scenarios.

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