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Common parametrizationmodels for cloud microphysical processes use condensate mass density and/or particle number density as prognostic properties. However, other moments of the particle size distribution can likewise be chosen for prediction. This study deals with parametrization models with one and two, respectively, prognostic moments for the sedimentation of drop ensembles. The spectral resolving model defines the reference solution. The evolution of the vertical profiles of liquid water content, drop number density and rain rate strongly depend on the choice of the prognostic moments in the parametrizationmodels. Inmodels with a single prognostic moment, its vertical profile is copied by all other moments. The moment of most physical pertinence is recommended for prediction. In models with two prognostic moments, the vertical profiles of all moments differ. The orders of the prognostic moments should be chosen close to the order of moments of highest relevance. Otherwise large errors occur. For example, comparison of modelled versus observed radar reflectivity (6th moment with respect to diameter) does not tell much about the quality of other properties if reflectivity is diagnosed from for example, number density and mass density. Furthermore, mass conservation is fulfilled only if mass density is forecasted.当前用于云微物理过程（cloud microphysical processes）的通用参数化模型，多以凝结物质量密度（condensate mass density）和/或粒子数密度（particle number density）作为预报量（prognostic properties）。然而，亦可选取粒子尺度分布（particle size distribution）的其他阶矩作为预报量。本研究聚焦于分别采用单、双预报矩（prognostic moments）的雨滴群沉降过程（sedimentation of drop ensembles）参数化模型，以分谱解析模型（spectral resolving model）作为参考解（reference solution）。 液态水含量（liquid water content）、雨滴数密度与降雨率（rain rate）的垂直廓线（vertical profiles）演变，强烈依赖于参数化模型中预报矩的选取。在仅采用单预报矩的模型中，其余所有阶矩的垂直廓线将直接复制该单矩的廓线。研究建议选取物理相关性最强的阶矩作为预报量。而在采用双预报矩的模型中，所有阶矩的垂直廓线各不相同。预报矩的阶数应尽可能选取与实际最高相关阶矩相近的数值，否则将引入显著误差。例如，若雷达反射率（radar reflectivity，关于直径的6阶矩）由数密度与质量密度反演得到，则通过对比模拟与观测的反射率，难以评估其他属性的模拟质量。此外，仅当对质量密度进行预报时，模型才能满足质量守恒（mass conservation）约束。