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. 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.
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:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.
AFDBench is a new benchmark that evaluates how well large language models can generate professional Area Forecast Discussions (AFDs) for the National Weather Service by reasoning through structured AI weather forecast data. It contains 7,732 expert-written discussions paired with real forecast inputs and introduces three metrics—Met-Align, Style-Align, and Input-Grounding—to assess numerical accuracy, professional dialect adherence, and fidelity to source data. Zero-shot tests show open-source LLMs perform poorly on style and grounding, but reinforcement learning with Group Relative Policy Optimization nearly doubles style alignment and improves grounding, enabling a 7B-parameter model to write like a professional meteorologist.
arXiv:2608. 09948v1 Announce Type: cross Abstract: No single AI weather model excels at all variables, pressure levels, and lead times.
arXiv:2607. 22385v1 Announce Type: cross Abstract: Diagnosing the root cause of anomalies is essential for safe industrial operation.
arXiv:2603. 11479v3 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-stakes domains.
arXiv:2512. 15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO).
arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syst...
arXiv:2606. 05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling.
arXiv:2511.20109v2 Announce Type: replace Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets...