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

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.

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.