首个专用于极端天气分析的智能代理框架，实现自动化诊断。
EWE: An Agentic Framework for Extreme Weather Analysis
- 构建知识引导的闭环推理框架，模拟专家分析流程。
- 在103个重大天气事件上实现全自动可视化诊断与解释。
- 适合气候研究者与灾害应对机构，推动科研普惠化。
极端天气事件对全球社会构成日益严峻的风险，亟需揭示其背后的物理机制。然而，当前依赖专家、耗时费力的诊断范式已形成关键瓶颈，制约科学进展。尽管人工智能在地球科学预测方面取得显著成果，但自动化诊断推理这一同等重要挑战仍鲜受关注。我们提出极端天气专家（EWE），首个专注于该任务的智能代理框架。EWE通过知识引导的规划、闭环推理和定制化的气象工具包，模拟专家工作流程，能自主从原始气象数据生成并解读多模态可视化结果，实现全面诊断分析。为推动领域发展，我们构建首个基准测试，包含103个高影响力事件的精选数据集及新型分步评估指标。EWE标志着迈向自动化科学发现的重要一步，有望赋能发展中国家，实现专业知识与资源的普及。
原文摘要 · Abstract (English)
Extreme weather events pose escalating risks to global society, underscoring the urgent need to unravel their underlying physical mechanisms. Yet the prevailing expert-driven, labor-intensive diagnostic paradigm has created a critical analytical bottleneck, stalling scientific progress. While AI for Earth Science has achieved notable advances in prediction, the equally essential challenge of automated diagnostic reasoning remains largely unexplored. We present the Extreme Weather Expert (EWE), the first intelligent agent framework dedicated to this task. EWE emulates expert workflows through knowledge-guided planning, closed-loop reasoning, and a domain-tailored meteorological toolkit. It autonomously produces and interprets multimodal visualizations from raw meteorological data, enabling comprehensive diagnostic analyses. To catalyze progress, we introduce the first benchmark for this emerging field, comprising a curated dataset of 103 high-impact events and a novel step-wise evaluation metric. EWE marks a step toward automated scientific discovery and offers the potential to democratize expertise and intellectual resources, particularly for developing nations vulnerable to extreme weather.
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