基于双偏振雷达准确反演雨滴谱能够为研究大范围的降水微物理特性提供丰富的数据。为了进一步提高雨滴谱反演的精度，本文提出了基于六、七阶矩的双阶矩规范化雨滴谱反演算法（M6M7法），利用2022年5-6月河源站双偏振雷达及周围雨滴谱仪观测的6次降水过程数据，从整体统计、不同降水强度和不同雨滴大小三个角度，将新的雨滴谱反演算法与基于三、六阶矩的双阶矩规范化雨滴谱反演算法（M3M6法）、约束性Gamma雨滴谱模型反演算法（C-G法）进行对比，并对每个算法的反演结果进行研究分析。结果表明，M6M7法在小雨阶段［0 mm·h-1<降水强度（R）≤5 mm·h-1］反演的滴谱参量误差是三种方法中最小的，随着降水强度的增大，除了液态水含量（LWC）、R误差增大，其余参量误差变化较小，在各个粒子端大部分参量误差在三种方法中最小，同时误差随粒子的增大变化较小。M3M6 法相较M6M7法增加了差传播相移率（Kdp）进行反演，虽Kdp对噪声敏感，但在大到暴雨（R>30 mm·h-1）环境中数据质量好，故在大到暴雨的反演具有较小的估测误差，且误差随粒子的增大（除LWC、R）有所减小。在中雨（5 mm·h-1<R≤30 mm·h-1）阶段，C-G 法偏差中值接近于 0，但部分误差变幅显著，且随降水强度和粒子增大，误差呈先减小后增大的趋势，相对误差波动剧烈，估测结果稳定性较差。整体评估显示，M6M7 法估测各滴谱参量的偏差中值更接近于 0 且误差波动范围小，M3M6 法和 C-G 法误差波动范围大，总之，新提出的M6M7法相较于传统方法整体反演效果更优且稳定性突出，尤其在中小雨阶段优势明显，而 M3M6 法在大到暴雨环境中反演效果更佳，C-G 法则表现出反演反演不稳定的特点。融合M6M7法和M3M6法的综合算法，经初步验证能进一步提升雨滴谱反演精度。

Accurate retrieval of raindrop size distributions（DSDs）based on dual-polarization radar can provide substantial data for the study of precipitation microphysical properties on a large scale. In order to further im‐ prove DSDs retrieval accuracy，this study proposes a new double-moment normalization method based on the sixth and seventh moments（M6M7 method），comparing it with the third and sixth moments method（M3M6 method）and the constrained Gamma model DSD retrieval method（C-G method）from three perspectives：over‐ all results，different rainfall intensities，and different rainfall particle sizes. Utilizing data from six rainfall events observed by dual-polarization radar and surrounding disdrometers at Heyuan station between May and June 2022，the retrieval results of each algorithm were analyzed. The results demonstrate that during light rain （0 mm·h-1<R≤5 mm·h-1），the M6M7 method exhibits the smallest parameter biases among the three methods. As rainfall intensity increases，biases for most parameters（except for the increase of liquid water content（LWC） and rainfall rate（R））remain relatively stable，with M6M7 consistently showing the lowest biases across different particle sizes and minimal fluctuation with the increase of particle size. Compared with M6M7 method，the M3M6 method incorporates specific differential phase shift on propagation（Kdp）for retrieval. Although Kdp is noise-sensitive，it has good quality in heavy rain（R>30 mm·h-1），resulting in smaller estimation biases for intense rainfall events and a decreasing trend in bias with larger particle sizes（excluding LWC and R）. For moderate rain（5 mm·h-1<R≤30 mm·h-1），the C-G method shows small median deviations yet significant fluctuations in certain parameters. With the increase of rainfall intensity and particle size，its biases shows a trend of first decreasing and then increase，accompanied by pronounced relative bias instability. Comprehensive evaluation results demonstrate that the M6M7 method consistently maintains median deviations approaching 0 across all DSD parameters，while exhibiting significantly tighter error fluctuation ranges. In marked contrast，both the M3M6 method and C-G method display substantially wider bias variability，with their error distributions spanning broader numerical ranges and demonstrating less stable performance characteristics. The newly proposed M6M7 method technique demonstrates advantages over traditional approaches，exhibiting enhanced comprehensive retrieval capabilities with regard to both accuracy and stability，particularly excelling in light-to-moderate rainfall with consistent accuracy. The M3M6 method proves more effective for heavy rain and storms，while the C-G method demonstrates unstable retrieval characteristics. The final section demonstrates the retrieval performance of the algorithm integrating both M6M7 and M3M6 methods，verifying its capability to further improve raindrop size distribution retrieval accuracy.