Identifying Individual Rain Events with a Dense Disdrometer Network
Identifying Individual Rain Events with a Dense Disdrometer Network
复制标题
使用密集的测速仪网络识别个别降雨事件
DOI：
10.1155/2015/582782
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发表时间：
2015
影响因子：
2.9
通讯作者：
J. Teves
中科院分区：
文献类型：
--
作者：
M. Larsen;J. Teves
使用点探测器测量降雨特性在水文科学中是普遍存在的。大多数降雨分析的早期步骤包括将数据记录划分为“降雨事件”。这项工作利用来自密集网络的光学disdrometers的数据，探讨仪器采样对这种分区的影响。它表明，抽样变异性可能会导致事件识别，可以在统计上放大两个相似的数据记录之间的差异。这里提供的数据表明，这些放大效应对降雨事件的所有常见定义的影响并不相同。
The use of point detectors to measure properties of rainfall is ubiquitous in the hydrological sciences. An early step in most rainfall analysis includes the partitioning of the data record into “rain events.” This work utilizes data from a dense network of optical disdrometers to explore the effects of instrument sampling on this partitioning. It is shown that sampling variability may result in event identifications that can statistically magnify the differences between two similar data records. The data presented here suggest that these magnification effects are not equally impactful for all common definitions of a rain event.