A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase $(K_{\mathrm{DP}})$ and to improve rain estimation. The hybrid $K_{\mathrm{DP}}$ estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase $(\delta_{\mathrm{hv}})$ dominates. Furthermore, a quantitative comparison between auto-weather-station rain-gauge observations and $K_{\mathrm{DP}}$- based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid $K_{\mathrm{DP}}$ estimator over existing methods.