2022年长江夏季热浪和干旱的三维DBSCAN探测及参数灵敏度

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Zhenchen Liu , Wen Zhou , Yuan Yuan
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引用次数: 3

摘要

准确的时空事件探测是探索极端气候机制的前提条件。为了实现这一目标,本研究采用了经典的无监督机器学习方法,即DBSCAN(基于密度的空间聚类应用噪声)聚类算法。此外,作者开发了一个基于三维(经纬度时间)dbscan的工作流程,用于目标气候极端事件检测和相关参数敏感性分析。基于ERA5再分析数据集,将基于三维dbscan的工作流程应用于2022年夏季长江极端热浪和干旱的检测。发现热浪和干旱具有不同的发展和迁移模式。天气尺度的极端热浪于6月底出现在北太平洋上空,向西南扩展,并于8月中旬几乎覆盖了整个长江流域。与此相反,7月中旬发生在孟加拉湾附近大陆地区的季节性干旱,向东北移动,并于9月中旬占据整个长江流域。事件探测可以在考虑发生、发展和迁移模式的同时,为气候机制提供新的见解。此外,作者还进行了详细的参数灵敏度分析,以便更好地理解算法的应用和结果的不确定性。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.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

Spatially and temporally accurate event detection is a precondition for exploring the mechanisms of climate extremes. To achieve this, a classical unsupervised machine learning method, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, was employed in the present study. Furthermore, the authors developed a 3D (longitude–latitude–time) DBSCAN-based workflow for event detection of targeted climate extremes and associated analysis of parameter sensitivity. The authors applied this 3D DBSCAN-based workflow in the detection of the 2022 summertime Yangtze extreme heatwave and drought based on the ERA5 reanalysis dataset. The heatwave and drought were found to have different development and migration patterns. Synoptic-scale heatwave extremes appeared over the northern Pacific Ocean at the end of June, extended southwestwards, and covered almost the entire Yangtze River Basin in mid-August. By contrast, a seasonal-scale drought occurred in mid-July over the continental area adjacent to the Bay of Bengal, moved northeastwards, and occupied the entire Yangtze River Basin in mid-September. Event detection can provide new insight into climate mechanisms while considering patterns of occurrence, development, and migration. In addition, the authors also performed a detailed parameter sensitivity analysis for better understanding of the algorithm application and result uncertainties.

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.

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来源期刊
Atmospheric and Oceanic Science Letters
Atmospheric and Oceanic Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.20
自引率
8.70%
发文量
925
审稿时长
12 weeks
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