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Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence

Hsieh, Chi-Wen, Chi, Po-Wei, Chen, Chih-Yen, Weng, Chun-Jen, Wang, Lijuan (2019) Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence. IEEE Transactions on Geoscience and Remote Sensing, . pp. 1-9. ISSN 0196-2892. (doi:10.1109/TGRS.2019.2933054) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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http://dx.doi.org/10.1109/TGRS.2019.2933054

Abstract

Rainfall measurement is subjected to various uncertainties due to the complexity of measurement techniques and atmosphere characteristics associated with weather type. Thus, this article presents a video-based disdrometer to analyze raindrop images by introducing artificial intelligence technology for the rainfall rate. First, a high-speed CMOS camera is integrated into a planar LED as a backlight source for appropriately acquiring falling raindrops in different positions. The falling raindrops can be illuminated and used for further image analysis. Algorithms developed for raindrop detection and trajectory identification are employed. In a field test, a rainfall event of 42 continuous hours has been measured by the proposed disdrometer that is validated against a commercial PARSIVEL² disdrometer and a tipping bucket rain gauge at the same area. In the evaluation for 5-min rainfall images, the results of the trajectory identification are within the precision of 87.8%, recall of 98.4%, and F1 score of 92.8%, respectively. Furthermore, the performance exhibits that the rainfall rate and raindrop size distribution (RSD) obtained by the proposed disdrometer are remarkably consistent with those of PARSIVEL² disdrometer. The results suggest that the proposed disdrometer based on the continuous movements of the falling raindrops can achieve accurate measurements and eliminate the potential errors effectively in the real-time monitoring of rainfall.

Item Type: Article
DOI/Identification number: 10.1109/TGRS.2019.2933054
Uncontrolled keywords: Disdrometer, In situ atmospheric observations, Particle tracking velocimetry (PTV), Raindrop size distribution, Rainfall rate
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Lijuan Wang
Date Deposited: 25 Oct 2019 15:44 UTC
Last Modified: 06 Feb 2020 04:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77840 (The current URI for this page, for reference purposes)
Wang, Lijuan: https://orcid.org/0000-0002-2517-2728
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