HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis
It addresses extreme weather diagnostics for meteorology, representing an incremental improvement by integrating expert knowledge into agentic systems.
The paper tackles the challenge of extreme weather diagnostics by proposing HVR-Met, a multi-agent system with a Hypothesis-Verification-Replanning mechanism, which excels in complex diagnostic scenarios as demonstrated experimentally.
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.