StarNet: A Deep Learning Model for Enhancing Polarimetric Radar Quantitative Precipitation Estimation

Accurate and real-time estimation of surface precipitation is crucial for decision-making during severe weather events and for water resource management. Polarimetric weather radar serves as the primary operational tool employed for quantitative precipitation estimation (QPE). However, the conventional parametric radar QPE algorithms overlook the dynamic spatiotemporal characteristics of precipitation. In addition, challenges such as radar beam attenuation and imbalanced distribution of precipitation data further compromise the estimation accuracy. This article develops a 3-D star neural network (StarNet) for polarimetric radar QPEs that integrate physical height prior knowledge and employ a reweighted loss function. To better cope with the dynamic characteristics of precipitation patterns, 3-D convolution is introduced within StarNet to effectively capture the spatiotemporal features between successive radar volume scanning data. In particular, multidimensional polarimetric radar observations are utilized as inputs, and surface gauge measurements are employed as training labels. The feasibility and performance of the StarNet model are demonstrated and quantified using U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) observations collected near Melbourne, Florida. The experimental results show that the StarNet model enhances the prediction accuracy of moderate to heavy precipitation events and improves the estimation performance over long distances, with a mean absolute error (MAE) of 1.55 mm, a root mean square error (RMSE) of 2.63 mm, a normalized standard error (NSE) of 25%, a correlation coefficient (CC) of 0.92, and a BIAS of 0.94 for hourly rainfall estimates. The results suggest that StarNet is able to effectively map the connection between polarimetric radar observations and surface rainfall.