用分层推理生成可解释的气象报告，提升预报准确性与可信度
Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting
- 分小时、6小时、日尺度进行多层级气象推理
- 关键词提取确保报告逻辑一致且事实准确
- 适合需要可解释性气象报告的科研与应用团队
我们提出层次化人工智能气象学家（Hierarchical AI-Meteorologist），一种基于大语言模型的智能体系统，通过分层预报推理与气象关键词生成，实现可解释的天气报告。不同于传统将预报视为平面时间序列的方法，该框架在小时、6小时和日尺度上进行多尺度推理，以捕捉短期动态与长期趋势。其核心推理智能体将结构化气象输入转化为连贯叙述，同时提取少数关键词，有效总结主导气象事件。这些关键词作为语义锚点，用于验证报告的一致性、时间连贯性和事实正确性。基于OpenWeather与Meteostat数据集的实验表明，分层上下文与关键词验证显著提升了大模型生成天气叙述的可解释性与鲁棒性，为自动化气象报告的语义评估提供了可复现框架，推动了基于智能体的科学推理发展。
原文摘要 · Abstract (English)
We present the Hierarchical AI-Meteorologist, an LLM-agent system that generates explainable weather reports using a hierarchical forecast reasoning and weather keyword generation. Unlike standard approaches that treat forecasts as flat time series, our framework performs multi-scale reasoning across hourly, 6-hour, and daily aggregations to capture both short-term dynamics and long-term trends. Its core reasoning agent converts structured meteorological inputs into coherent narratives while simultaneously extracting a few keywords effectively summarizing the dominant meteorological events. These keywords serve as semantic anchors for validating consistency, temporal coherence and factual alignment of the generated reports. Using OpenWeather and Meteostat data, we demonstrate that hierarchical context and keyword-based validation substantially improve interpretability and robustness of LLM-generated weather narratives, offering a reproducible framework for semantic evaluation of automated meteorological reporting and advancing agent-based scientific reasoning.
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