2026/08/01 by Kazuhiko Tamesue, Zheng Wen, Shotaro Yamaguchi +6
Engineering · #eess.SP
paper · pdf · doi:10.1109/cama57522.2023.10352672
arxiv created 2026/08/01 · arxiv updated 2026/08/04
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.