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中国安徽省浓雾的基于机器学习的短期预报方法
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
まとめ
本研究开发了一种用于安徽省浓雾预测的随机森林模型,取得了高精度,并且优于现有方法。该模型利用气象数据来预测雾的发生,提高了区域安全。
科学分野:
- 气象学
- 大气科学
- 数据科学
背景:
- 区域性浓雾对交通安全构成重大风险。
- 准确的浓雾短期预报对于减轻这些风险至关重要。
研究 の 目的:
- 开发和评估用于安徽省短期浓雾预报的机器学习模型。
- 识别影响浓雾形成的关键气象因素。
主な方法:
- 利用了每小时气象观测数据和ERA5再分析数据(2011-2023年)。
- 采用旋转经验正交函数(REOF)划分了七个雾区。
- 开发并比较了五种机器学习算法(随机森林、逻辑回归、K-NN、高斯朴素贝叶斯、决策树)。
主要な成果:
- 安徽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报能力。
- 随机森林(RF)模型取得了最高的威胁分数(TS)和准确性。
- RF的关键预测因子包括水蒸气、热力学以及上下游效应。
- RF模型在TS和漏警率方面优于ECMWF和SCMOC。
結論:
- RF模型是安徽省短期浓雾预报的首选方法。
- 使用REOF进行客观分区可以增强区域预报能力。
- RF模型与现有的业务产品相比,表现更优。
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