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Natural seeder-feeder is a common phenomenon in stratiform clouds precipitation. The quantitative evaluation of the enhancement effect of seeder-feeder plays an important role in improving the accuracy of weather forecasting and artificial weather operations. In this study, ground-based millimeter-wave cloud radar, microwave radiometer (MWR), and ground precipitation phenomenon instrument (GPPI) were used to observe the natural seeder-feeder process between double-layer clouds during ground snowfall in Xi’an, China for three years. A constrained small particle tracing method and an optimized fuzzy logic algorithm were proposed to achieve inversion of vertical air motions ( $V_{a}$ ) and particle terminal falling velocity ( $V_{f}$ ) and identify cloud phase states. The reflectivity factor (Z) (attenuation corrected) and mean Doppler velocity ( $V_{r}$ ) were used to identify the feeding and nonfeeding areas in low-level feeding clouds. The enhancement effect of seeding on cloud particles was quantitatively evaluated by analyzing the differences in Z, $V_{f}$ , and particle diameter (D) between the feeding and nonfeeding areas. Meanwhile, the enhancement effect of seeding on ground precipitation was analyzed. The statistical results show that after seeding, both cloud particles and ground precipitation significantly increased. The average-Z, $- V_{f}$ , and $- D$ of cloud particles in the feeding areas have increased by 10 dBZ, 0.75 m $\cdot $ s−1, and 0.5 mm compared with the nonfeeding areas, respectively. Compared with the nonfeeding period, the average ice water content (IWC), average number concentration ( $N_{T}$ ), mean particle diameter ( $D_{m}$ ), and maximum particle diameter (MPD) of ground precipitation particles during the feeding periods were [1.01–67.91], [1.04–35.37], [1.04–2.04], and [1.01–3.65] times, respectively.