Polarimetric Retrieval of Raindrop Size Distribution: Double-Moment Normalization Approach and Machine Learning Techniques

Retrieving raindrop size distribution (DSD) is essential to understanding precipitation processes. Conventional approaches based on polarimetric radar (e.g., polynomial regression) struggle to accurately capture the inherent nonlinearity between DSD parameters and radar measurables. In contrast, machine learning (ML) algorithms offer a promising solution as it effectively models the complex non-linear relationship. We have developed an ML algorithm to retrieve DSD parameters using polarimetric radar variables in a framework of double-moment normalization. The potentially stable and invariant double-moment normalized DSD enables the applicability of the algorithm in any climatic regime or any precipitation system. To improve the robustness of the model to measurement noises, we employed training samples with random noise. All ML algorithms outperformed the conventional method, with the random forest being the best model. This study highlights the effectiveness of the developed algorithm as a tool for understanding the DSD characteristics from polarimetric radar measurements.