Precipitation is a key component of the hydrologic cycle, so the knowledge and comprehension of its processes of formation and evolution are of paramount importance. This study presents an analysis of microphysical data and drop size distributions (DSD) collected with PWS100 optical disdrometers at four sites in Mexico. Bulk parameters (accumulated water and rainfall intensity) estimated from microphysical data (drop size and fall speed) showed good agreement with observations obtained using rain gauges installed next to the optical devices. Estimates of accumulated water were used to evaluate the efficiency of four different methods of moments for the fitting of the DSD gamma parameters. The outcomes reveal that the second, fourth, and sixth DSD moments method has a better performance (by showing the lowest RMSE value with respect to the measurements) and yields the closest results to those for the Γ–normalized DSD parameterization. Also, a new method for rain classification based on the variation of accumulated water and median volume diameter (D₀), which is used as an estimator of the DSD width, is proposed. The method allows for the characterization of rain into three types (convective, stratiform, and shallow), and results can be related to previous observations from DSD parameters (N₀ jumps). The study shows that all three gamma DSD parameters (N₀, Λ and μ) have large values when the spectra are dominated by large numbers of small and medium-sized drops (low R values), and smaller values when DSD shifts toward large diameters as R increases. Furthermore, the averages of the gamma parameters reveal lower, intermediate and greater values as rain is classified as convective (intense), stratiform or shallow (very light), respectively. The opposite behavior is observed for D₀, which agrees with the previous results. These outcomes reveal that continuous research is necessary to refine the tendencies of DSD parameters, even at different places, with rainfall intensity or type of rain and for the improvement of our knowledge of rain processes.

降水是水文循环的关键组成部分，因此对其形成和演化过程的了解和理解至关重要。这项研究提供了在墨西哥四个地点使用PWS100光学测距仪收集的微物理数据和液滴尺寸分布（DSD）的分析结果。根据微物理数据（液滴大小和下降速度）估算的体积参数（累积的水和降雨强度）与使用安装在光学设备旁边的雨量计获得的观测结果显示出很好的一致性。累积水的估算值用于评估DSD伽玛参数拟合的四种不同矩量法的效率。结果表明，第二，第四，第六种DSD矩方法具有更好的性能（通过显示相对于测量值的最低RMSE值），并且得出的结果与Γ标准化DSD参数化的结果最接近。另外，一种基于累积水量和中位体积直径变化的降雨分类新方法（提出了用作DSD宽度的估计器的D ₀）。该方法可以将降雨表征为三种类型（对流，层状和浅层），其结果可能与DSD参数的先前观测值有关（N ₀跳变）。研究表明，当光谱以大量的中小液滴（低R值）为主导时，所有三个伽玛DSD参数（N ₀，Λ和μ）都具有较大的值，而当DSD朝大直径方向移动时，所有参数都较小作为R增加。此外，随着雨水分别分类为对流（强烈），层状或浅（非常轻），伽玛参数的平均值显示出较低，中间和较大的值。对于D ₀观察到相反的行为，这与先前的结果一致。这些结果表明，有必要进行持续的研究以完善DSD参数的趋势，即使在不同的地方，也要采用降雨强度或降雨类型，并改善我们对降雨过程的了解。