Machine-Learning-Based Radar Quantitative Precipitation Estimation Using Intra-Hour Temporal Features from Dual-Polarization Observations

Hourly precipitation is a standard variable for hydrological applications, flood warning, and quantitative assessment of precipitation-related hazards. Weather radar provides high-frequency observations, but these sub-hourly measurements must be linked to hourly rain-gauge accumulations for radar-based quantitative precipitation estimation (QPE). This study develops a machine-learning-based hourly radar QPE framework using intra-hour temporal features from dual-polarization observations. Each hourly gauge accumulation was matched with ten consecutive 6 min radar observations. Six temporal features were derived from dual-polarization observations, including the mean, sum, standard deviation, linear trend, maximum, and skewness. The results show clear rainfall regime dependence in the contribution of polarimetric variables. Evaluation of machine-learning models confirms the value of intra-hour temporal features, with the best-performing model achieving an RMSE of 3.96 mm h−1, compared with 6.42 mm h−1 for the linear baseline. The experiments further show that the complete temporal feature set improves QPE performance. Diagnostic evaluation also indicates that model bias is sensitive to the representation of heavy rainfall samples. These findings suggest that ML-based radar QPE should emphasize physically meaningful intra-hour polarimetric features and rainfall-imbalance-aware sampling.