arXiv:2607. 24588v1 Announce Type: new Abstract: Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events.
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The paper introduces SmartWeatherAgent, a three‑stage architecture that combines intent recognition, hazard prediction, and reasoning‑enhanced generation to improve tourism meteorological services. It fuses rule‑based methods with large language models and a LightGBM model enriched with highland‑specific features, achieving an F1‑Macro score of 0.605 and 1.60 ms latency on high‑wind, precipitation, and low‑temperature events. A 12‑round micro‑step prompt self‑optimization loop raises the composite warning quality score from 4.2 to 8.9, with notable gains in data source citation, physical mechanism explanation, and scientific rigor through explicit uncertainty statements.
To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integr...
arXiv:2603. 01121v2 Announce Type: replace Abstract: While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge.
arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.
arXiv:2511.20109v2 Announce Type: replace Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets...
arXiv:2608. 11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems.