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

Shuo Tang, Jiadong Zhang, Gengxian Zhou, Qizhao Jin, Qinxuan Wang, Yi Hu, Ning Hu, Hongchang Ren, Lingli He, Shiming Xiang, Jingtao Ding, Jian Xu, Jiaolan Fu, Cheng-Lin Liu

TL;DR: ~Shuo_Tang3

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 sub-tasks. Experimental evidence demonstrates that the system excels in complex diagnostic scenarios.

Lay Summary: While deep learning has advanced weather forecasting, extreme weather diagnostics remains challenging due to its reliance on complex, multi-step logical reasoning and expert-level prior judgment. To address this, we propose HVR-Met, a multi-agent meteorological diagnostic system deeply integrated with expert knowledge. Its core innovation is a "Hypothesis-Verification-Replanning" closed-loop mechanism designed for sophisticated iterative reasoning on anomalous meteorological signals. Excelling in complex diagnostic scenarios, HVR-Met has reached the diagnostic proficiency of a junior forecaster, proving its capability to reliably assist human experts in diagnosing extreme weather events.

Primary Area: Applications->Chemistry, Physics, and Earth Sciences

Keywords: ERA5, Extreme Weather Diagnosis

Submission Number: 3286