从中国历史天气记录中使用优化自然语言处理进行高分辨率的气候重建
1School of Economics and Management, Shanxi University, 63 Nan Zhonghuan East Street, Taiyuan City, 030031, Shanxi Province, China. litang0950@gmail.com.
Scientific reports
|December 24, 2025
概括
本研究引入了一种使用自然语言处理 (NLP) 来将历史天气记录转换为定量气候数据的新框架. 这种方法增强了对过去气候变化及其社会影响的理解.
科学领域:
- 历史气候学 历史气候学
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 重建历史气候数据是具有挑战性的,因为主观性和缺乏标准化.
- 现有的方法很难捕捉历史文档中天气描述的细微差别.
研究的目的:
- 开发和验证一个新的框架,用于从历史文件中重建高分辨率的气候数据.
- 在历史气候数据重建中克服主观性和标准化问题.
主要方法:
- 使用优化自然语言处理 (NLP) 技术,语义聚类和动态扩展构建了一个历史天气分类词典.
- 开发了一个多维指数系统来量化历史天气,包括强度,农业,经济,社会影响和伤亡等指标.
- 使用方法和专家判断分配目标权重.
- 使用来自中国广东和河北省历史文件的低温天气记录验证了框架.
主要成果:
- 该框架成功地将定性历史叙述转化为定量气候数据.
- 广东和河北重建的低温天气趋势与现有的清朝气候变化研究一致.
- 省级趋势图显示了同步的变化模式和显著的区域差异.
- 一个随机森林模型实现了94.0%的分类准确度和低级事件的AUC得分超过0.98.
结论:
- 数据驱动的方法提供了一个可复制和可扩展的工具,用于历史气候数据的重建.
- 增强对过去气候变化及其社会影响的理解.
- 为分析历史天气记录提供了一种标准化的方法.
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