TY - GEN
T1 - A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar
AU - Tamesue, Kazuhiko
AU - Wen, Zheng
AU - Yamaguchi, Shotaro
AU - Kasai, Hiroyuki
AU - Kameyama, Wataru
AU - Sato, Toshio
AU - Katsuyama, Yutaka
AU - Sato, Takuro
AU - Maesaka, Takeshi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Cloud Radar
KW - Machine Learning
KW - Terahertz
UR - http://www.scopus.com/inward/record.url?scp=85182267742&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182267742&partnerID=8YFLogxK
U2 - 10.1109/CAMA57522.2023.10352672
DO - 10.1109/CAMA57522.2023.10352672
M3 - Conference contribution
AN - SCOPUS:85182267742
T3 - IEEE Conference on Antenna Measurements and Applications, CAMA
SP - 376
EP - 380
BT - 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
PB - Institute of Electrical and Electronics Engineers
T2 - 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
Y2 - 15 November 2023 through 17 November 2023
ER -