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Advances in Meteorology
Volume 2015, Article ID 582782, 12 pages
Research Article

Identifying Individual Rain Events with a Dense Disdrometer Network

Department of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, SC 29424, USA

Received 2 July 2014; Accepted 5 October 2014

Academic Editor: Francisco J. Tapiador

Copyright © 2015 Michael L. Larsen and Joshua B. Teves. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.