Accurate and early detection of Localized Heavy Rain by integrating multivendor sensors in various installation environments

K. Hiroi, Yoshihito Seto, Futoshi Matsumoto, Yuzo Taenaka, Hideya Ochiai, Haruo Ando, Hitoshi Yokoyama, Masaya Nakayama, Hideki Sunahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


In this study, we focus on the accurate and early prediction of Localized Heavy Rain (LHR) using multiple sensors. Traditional sensors, such as rain gauges and radar, cannot detect LHR until cumulonimbus clouds cover the sensors. In contrast, Surface Meteorological Monitoring Networks (SMMNs) can accurately measure rainfall in the vicinity of the sensors, thereby detecting LHR earlier than traditional sensors. By evenly placing the sensors around a large city, a SMMN should be useful in predicting LHR. However, since most sensors are placed in a different installation environment, their raw sensor data may significantly differ depending on their surrounding environment (i.e., altitude and sky view factor). Therefore, we propose a calibration scheme for a SMMN that utilizes many sensors in various installation environments and implement a novel LHR prediction system that produces accurate and early LHR predictions. Our system proved to accurately predict LHR 30 minutes earlier than traditional schemes.

Original languageEnglish
Title of host publicationIEEE SENSORS 2013 - Proceedings
PublisherIEEE Computer Society
ISBN (Print)9781467346405
Publication statusPublished - 2013
Event12th IEEE SENSORS 2013 Conference - Baltimore, MD, United States
Duration: 2013 Nov 42013 Nov 6

Publication series

NameProceedings of IEEE Sensors


Other12th IEEE SENSORS 2013 Conference
Country/TerritoryUnited States
CityBaltimore, MD

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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