EWE: An Agentic Framework for Extreme Weather Analysis
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911288245878784 |
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| author | Jiang, Zhe Wang, Jiong Yue, Xiaoyu Guo, Zijie Zhang, Wenlong Ling, Fenghua Ouyang, Wanli Bai, Lei |
| author_facet | Jiang, Zhe Wang, Jiong Yue, Xiaoyu Guo, Zijie Zhang, Wenlong Ling, Fenghua Ouyang, Wanli Bai, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EWE: An Agentic Framework for Extreme Weather Analysis Jiang, Zhe Wang, Jiong Yue, Xiaoyu Guo, Zijie Zhang, Wenlong Ling, Fenghua Ouyang, Wanli Bai, Lei Artificial Intelligence Atmospheric and Oceanic Physics 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. |
| title | EWE: An Agentic Framework for Extreme Weather Analysis |
| topic | Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2511.21444 |