一种基于机器学习的短期预测方法,用于预测中国安省大雾的情况
Yan Sun1,2, Chuanhui Wang3,4, Yeqing Yao1,2
1Anhui Public Meteorological Service Center, No.16 Shihe Road, Hefei, 230031, China.
Scientific reports
|January 13, 2026
概括
这项研究开发了一种随机森林模型,用于安省的大雾预测,实现了高精度和优于现有方法的性能. 该模型利用气象数据预测雾发生情况,改善区域安全.
科学领域:
- 气象学 天气学
- 大气科学 大气科学
- 数据科学数据科学数据科学
背景情况:
- 区域重雾对运输安全构成重大风险.
- 准确的重雾短期预测对于减轻这些风险至关重要.
研究的目的:
- 开发和评估用于安省短期重雾预报的机器学习模型.
- 确定影响重雾形成的关键气象因素.
主要方法:
- 利用每小时的气象观测和ERA5再分析数据 (2011-2023年).
- 使用旋转实证直角函数 (REOF) 划分七个雾区.
- 开发并比较了五种机器学习算法 (随机森林,物流回归,K-NN,高斯天真贝叶斯,决策树).
主要成果:
- 安省被客观地划分为七个不同的雾区.
- 所有测试的机器学习模型都显示出预测技能.
- 随机森林 (RF) 模型实现了最高的威胁评分 (TS) 和准确性.
- 射频的关键预测因素包括水蒸气,热力学和上下游效应.
- 射频模型在TS和缺失报警率方面表现优于ECMWF和SCMOC.
结论:
- 射频模型是安省短期重雾预报的首选方法.
- 使用REOF的目标区划增强了区域预测能力.
- 与现有运营产品相比,RF模型表现出优越的性能.
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