A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar

Kazuhiko Tamesue*, Zheng Wen, Shotaro Yamaguchi, Hiroyuki Kasai, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato, Takeshi Maesaka

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Accurate measurement of non-precipitable clouds is important for early prediction of heavy rainfall disasters caused by extreme weather events. However, microwave cloud radar cannot observe the early stages of cloud development from non-precipitation clouds (cumulus) to cumulonimbus. In this paper, we propose a terahertz dual-frequency cloud radar using 150 GHz and 95 GHz bands to detect cloud particles in cumulus smaller than 10 μm. Using a dataset generated by the ITU-R radio propagation model, we estimate the liquid water content of non-precipitation clouds and water vapor content in atmospheric gases, respectively, by using a machine learning-based approach. The effectiveness of using the dual wavelength ratio as an explanatory variable is examined.

Original languageEnglish
Title of host publication2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages376-380
Number of pages5
ISBN (Electronic)9798350323047
DOIs
Publication statusPublished - 2023
Event2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023 - Genoa, Italy
Duration: 2023 Nov 152023 Nov 17

Publication series

NameIEEE Conference on Antenna Measurements and Applications, CAMA
ISSN (Print)2474-1760
ISSN (Electronic)2643-6795

Conference

Conference2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
Country/TerritoryItaly
CityGenoa
Period23/11/1523/11/17

Keywords

  • Cloud Radar
  • Machine Learning
  • Terahertz

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation
  • Radiation

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