3D DBSCAN detection and parameter sensitivity of the 2022 Yangtze river summertime heatwave and drought

被引：0
|
作者：
Zhenchen Liu [1 ]
Wen Zhou [1 ]
Yuan Yuan [2 ]
机构：
[1] Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences,Fudan University
[2] China Meteorological Administration Key Laboratory for Climate Prediction Studies,National Climate Center
来源：
Atmospheric and Oceanic Science Letters | 2023年 / 16卷 / 04期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
DBSCAN算法; 复合气候极值事件; 高温干旱; 长江流域; 机器学习;
D O I：
暂无
中图分类号：
P426.616 [降水引起的灾害];
学科分类号：
0706 ; 070601 ;
摘要：
极端气候事件的精准识别是机理分析的重要前提.本研究借助无监督机器学习中经典的DBSCAN密度聚类算法,发展了在三维(经度-纬度-时间)空间内进行目标事件识别和参数敏感性分析的研究方案.在2022年长江全域高温伏秋旱事件识别中的应用表明,本次天气尺度极端热浪和季节尺度重旱事件的产生发展,空间传播模式不同.天气尺度热浪信号自6月底从北太平洋向西南方向延伸,直至8月中旬覆盖长江全域;季节重旱信号于7月中旬从孟加拉湾陆面区域向东北向延伸,直至9月中旬覆盖长江全域.同时,本研究中亦进行了相关参数敏感性的详细分析,对算法应用,结果理解亦有帮助.
引用
收藏
页码：17 / 23
页数：7
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