Date of Award
8-1-2025
Degree Name
Doctor of Philosophy
Department
Engineering Science
First Advisor
Wang, Yadong
Second Advisor
Lu, Chao
Abstract
Accurate retrieval and classification of precipitation are essential for advancing hydrologic modeling, weather forecasting, and climate monitoring. This dissertation presents two methodologies that integrate artificial intelligence with multi-frequency and polarimetric radar observations to enhance radar-based precipitation processing.The first component focuses on retrieving drop size distribution (DSD) parameters using dual-polarization radar at S- and C-band frequencies. An optimization framework combining particle swarm optimization (PSO) and T-Matrix scattering simulations is developed to estimate DSD parameters from radar observations. Validation with a network of OTT PARSIVEL disdrometers and radar data from a unique observational configuration in Taiwan shows a 30% improvement in quantitative precipitation estimation (QPE) accuracy over traditional techniques.The second component introduces a deep learning classification system using vertical profiles of reflectivity from the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR). A deep neural network (DNN) classifies hydrometeors into stratiform, convective, hail, and snow classes. Compared to the GPM DPR Version 07 data product, the DNN improves precision from 0.70 to 0.87 and recall from 0.67 to 0.88.These results demonstrate that artificial intelligence can significantly improve both ground-based and satellite-based radar data processing, enabling more accurate and efficient precipitation retrieval and classification.
Access
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