Identifying Individual Rain Events with a Dense Disdrometer Network
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 effect...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/582782 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849399401220407296 |
|---|---|
| author | Michael L. Larsen Joshua B. Teves |
| author_facet | Michael L. Larsen Joshua B. Teves |
| author_sort | Michael L. Larsen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a30bf28a7e04464481a9fd3e405321c8 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-a30bf28a7e04464481a9fd3e405321c82025-08-20T03:38:19ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/582782582782Identifying Individual Rain Events with a Dense Disdrometer NetworkMichael L. Larsen0Joshua B. Teves1Department of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, SC 29424, USADepartment of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, SC 29424, USAThe 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.http://dx.doi.org/10.1155/2015/582782 |
| spellingShingle | Michael L. Larsen Joshua B. Teves Identifying Individual Rain Events with a Dense Disdrometer Network Advances in Meteorology |
| title | Identifying Individual Rain Events with a Dense Disdrometer Network |
| title_full | Identifying Individual Rain Events with a Dense Disdrometer Network |
| title_fullStr | Identifying Individual Rain Events with a Dense Disdrometer Network |
| title_full_unstemmed | Identifying Individual Rain Events with a Dense Disdrometer Network |
| title_short | Identifying Individual Rain Events with a Dense Disdrometer Network |
| title_sort | identifying individual rain events with a dense disdrometer network |
| url | http://dx.doi.org/10.1155/2015/582782 |
| work_keys_str_mv | AT michaelllarsen identifyingindividualraineventswithadensedisdrometernetwork AT joshuabteves identifyingindividualraineventswithadensedisdrometernetwork |