基于机器学习的太赫兹双频雷达非降水云估算方法
A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar
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中文总结 AI 辅助
针对微波云雷达无法观测非降水云发展早期的问题，提出基于太赫兹双频雷达与机器学习的方法，结合ITU-R数据集估算非降水云液态水含量及水汽含量，验证双波长比作为解释变量的有效性。
中文摘要 AI 辅助
精确测量非降水云对极端天气事件引发的强降雨灾害早期预测至关重要，但微波云雷达无法观测从非降水云（积云）到积雨云的云发展早期阶段。本文提出采用150GHz和95GHz频段的太赫兹双频云雷达，用于探测小于10μm的积云中的云粒子；基于ITU-R无线电传播模型生成的数据集，采用机器学习方法分别估算非降水云的液态水含量和大气气体中的水汽含量，并验证了使用双波长比作为解释变量的有效性。
英文摘要
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.
