Leveraging Deep Learning to Extract Raindrop Size Gamma Distribution Parameters from Wind Profile Radar Data

Raindrop size distribution (DSD) is crucial in the study of precipitation microphysics, but the parameters that characterize the DSD is not easy to be derived accurately. Wind profile radar (WPR) provides rich data with high spatiotemporal resolution and low attenuation, and the previous studies using WPR power spectral distribution to extract DSD parameters demonstrate certain limitations. In this study, a WPR-based Gamma DSD parameter estimation network (WPR-DSDnet) combined with a neural network (NN) and a long short-term memory (LSTM) network is designed, and then a WPR-DSDnet-based model is trained to retrieve the Gamma DSD parameters including normalized intercept parameter (N_w) and mass-weighted average diameter (D_m) by means of the high-resolution WPR reflectivity factor and velocity spectral width as the inputs, and the spatiotemporal matching disdrometer data collected from 2017 to 2021 in Beijing as labels. Two precipitation cases are used to validate the performance of the model, and the results demonstrate that the model can retrieve the Gamma DSD parameters effectively, in which the ratio between the estimated and measured values of both lgN_w and D_m exceeds 99%. The estimation accuracy of lgN_w is slightly better than that of D_m due to smaller relative deviation of lgN_w although the absolute deviation between the estimated and true values of lgN_w is larger than that of D_m, which are probably caused by the larger distribution range of lgN_w. The vertical distributions of lgN_w closely aligns with variations in precipitation intensity, which hints it is a useful indicator for precipitation intensity change. On the other hand, the distribution of D_m is closely associated with the degree of convection, which is valuable for precipitation recognition and classification. The two parameters extracted by the deep learning-based model can facilitate further in-depth analysis on precipitation characteristics and mechanisms with WPR data.