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A scheme for rain gauge network design based on remotely sensed rainfall measurements

Dai, Qiang, Bray, Michaela, Zhou, Lu, Islam, Tanvir and Han, Dawei 2017. A scheme for rain gauge network design based on remotely sensed rainfall measurements. Journal of Hydrometeorology 18 (2) , pp. 363-379. 10.1175/JHM-D-16-0136.1

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Abstract

We have been facing a remarkable decline in the number of raingauges in many areas of the world, as a compromise to the expensive cost of operating and maintaining raingauges. The question of how to effectively deploy new or remove current raingauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled remotely-sensed rainfall measurements, although poorer quality compared with traditional raingauge rainfall measurements, provide an insight into the local storm characteristics, which traditional methods for designing a raingauge network sort to seek. Based on these facts, this study proposes a new methodology for raingauge network design using remotely-sensed rainfall data set, which aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyse the redundancy of the radar grids network and determine the number of raingauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grids network, and compared against an existing dense raingauge network in Southwest England. Due to the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into raingauge network design, which is also a preliminary attempt of using remotely-sensed data to solve the traditional raingauge problems.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: American Meteorological Society
ISSN: 1525-755X
Date of Acceptance: 10 November 2016
Last Modified: 10 Aug 2020 10:30
URI: http://orca.cf.ac.uk/id/eprint/96949

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