A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar
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 {\mu}m. 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.
Cite
@article{arxiv.2608.00653,
title = {A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar},
author = {Kazuhiko Tamesue and Zheng Wen and Shotaro Yamaguchi and Hiroyuki Kasai and Wataru Kameyama and Toshio Sato and Yutaka Katsuyama and Takuro Sato and Takeshi Maesaka},
journal= {arXiv preprint arXiv:2608.00653},
year = {2026}
}