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

TL;DR
HVR-Met is a multi-agent system that employs a hypothesis-verification-replanning loop to improve extreme weather diagnostics through expert knowledge integration and iterative reasoning.
Contribution
The paper introduces HVR-Met, a novel multi-agent framework with a closed-loop reasoning mechanism for enhanced extreme weather diagnosis.
Findings
System outperforms existing methods in complex diagnostic scenarios
Effective integration of expert knowledge improves reasoning accuracy
New benchmark evaluates atomic-level subtasks in weather diagnostics
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…
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Taxonomy
TopicsMeteorological Phenomena and Simulations · Hydrological Forecasting Using AI · Data Visualization and Analytics
