Most of recent hydrometeor classification schemes for dual-polarimtric radar are based on fuzzy logic. Due to the lack of true value of hydrometeors, it is difficult to verify whether classifications are right or not. Therefore, three methods are proposed, cumulative value test, algorithm sensitivity test and hydrometeors' distribution test. Cumulative value test is used to inspect the ability of classifying and distinguishing. When input data contains system deviation and noise, the output classification would also contain system deviation and noise. Hence, a method is proposed to test the sensitivity of system deviation and noise. Hydrometeor distribution test is to analyze whether the hydrometeor distribution is temporal and spatial continuous. Through these tests the reliability and stability of the algorithm are analyzed, and key factors which affect the classification can be found out. Using observations of precipitation processes in Guangzhou in spring and summer from 2016 to 2017, the efficiency of S-band dual-polarization doppler radar is examined. Main results show that the classifying hydrometeor relies on the membership function. Using cumulative value test, some hydrometeors are found out with inappropriate membership function. These membership functions are not consistent with real characteristics of hydrometeors, which are ground clutter (including that due to anomalous propagation), biological scatters, dry aggregated snow, crystals of carious orientations, light and moderate rain, and a mixture of rain and hail. The way to modify these membership functions is based on hydrometeor statistic characteristics. Another insufficiency of membership function is to distinguish similar hydrometeors, such as crystals of various orientations with dry aggregated snow and heavy rain with mixture of rain and hail. The method to modify membership function's distinguishing ability is increasing parametric weights which has stronger discriminating ability. To ensure every hydrometeor has more than 90% stable results, the error of Zh is between -0.5 dB and +0.5 dB, that of ZDR is between -0.1 dB and +0.1 dB, that of ρhv is less than 0.02, and that of KDP is between -0.3 dB and +0.9 dB. Moreover, data quality of Zh and ZDR is more important than other parameters, system deviation is more influencing than noise. After hydrometeor distribution test, it is found that heavy rain may be misclassified as mixture of rain and hail. The spatial consistency correction method is to check whether the distribution of rain and hail mixture in a certain space is continuous.