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

Dai, Qiang, Bray, Michaela ORCID:, Zhuo, 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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A remarkable decline in the number of rain gauges is being faced in many areas of the world, as a compromise to the expensive cost of operating and maintaining rain gauges. The question of how to effectively deploy new or remove current rain gauges 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 rain gauge rainfall measurements, provide an insight into the local storm characteristics, which are sought by traditional methods for designing a rain gauge network. Based on these facts, this study proposes a new methodology for rain gauge network design using remotely sensed rainfall datasets that aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyze the redundancy of the radar grid network and to determine the number of rain gauges 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 grid network and compared against an existing dense rain gauge network in southwestern England. Because of the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into rain gauge network design that is also a preliminary attempt to use remotely sensed data to solve the traditional rain gauge problems.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Publisher: American Meteorological Society
ISSN: 1525-755X
Date of Acceptance: 10 November 2016
Last Modified: 27 Oct 2022 15:00

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