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The evolution of a hydrometeor ensemble (,,cloud" ) can be described using a balance equation for its size spectrum. In numerical weather prediction or climate models, however, this approach is too time consuming. It is therefore necessary to capture, if only approximately, the on-going microphysical processes in a cloud using a parame- terised form of modelling. The parameterisation of sedimentation alone is already a demanding task. If its standard form is used in a two-moment scheme, the mean mass of the hydrometeors will be too large for a cloud physics context. Existing approaches try to avoid excessively large mean masses by altering dynamically the parameterisation assumptions in the running model.
In this work, a new fundamental approach is presented: the assumption of a spectrum containing particles of all sizes is replaced by its truncation at a particle size realistic in cloud physics. The calculation of integrals over the truncated spectrum requires a new technique, which demands higher computational effort.
In this work, comparisons of the new parameterisation with already existing parameterisations are made while changing the initial conditions and the advection scheme. With the new method, the parameterisation error is reduced by up to 50 %. The new paramerisation also constitutes a great improvement compared to the standard form when transferred to modelling an ensemble of solid hydrometeors. This is illustrated using a particle type commonly found in polar regions. Furthermore, for the first time a B-distribution is used as a basis for a cloud microphysics parameterisation. Its domain of definition is bounded by construction. This distribution, however, appears not suitable for use in two-moment schemes, because one free parameter has to be derived from model data.
Extending the sedimentation model with drop collisions and starting with a cloud droplet spectrum, it is not possible to judge the quality of the sedimentation paramete- risations, because the coagulation rates are dominated by the choice of the sedimetation velocity for small droplets. If the initial spectrum already contains a sufficient number of raindrops, however, application of the new method again reduces the parameterisation error by up to 50 %.

水凝物集合（hydrometeor ensemble，即云团）的演变可通过其尺度谱的平衡方程进行描述。然而在数值天气预报（numerical weather prediction）与气候模式（climate models）中，该方法的计算耗时过长。因此即便仅能近似刻画，也需要通过参数化建模的方式来捕捉云内持续发生的微物理过程。仅针对沉降过程的参数化设计，就已是一项极具挑战性的工作。若在双矩方案（two-moment scheme）中采用其标准形式，水凝物的平均质量会超出云物理学语境下的合理范围。现有方法试图通过在运行的模式中动态调整参数化假设，来避免平均质量过大的问题。
本研究提出了一种全新的基础方法：将包含所有尺度粒子的尺度谱假设，替换为在云物理学中具有实际物理意义的粒子尺度处截断该谱的做法。对截断后的尺度谱进行积分计算，需要采用新的技术，这会带来更高的计算开销。
本研究在改变初始条件与平流方案（advection scheme）的前提下，将新参数化方案与现有参数化方案进行了对比。采用新方法后，参数化误差最高可降低50%。当将该方案推广至固态水凝物集合的模拟时，相较于标准形式，新参数化方案同样实现了大幅优化。这一点可通过极地地区常见的一种粒子类型进行验证说明。此外，本研究首次将B分布（B-distribution）作为云微物理参数化的基础分布。该分布的定义域由其构造方式所限定，但似乎并不适用于双矩方案，因为其需要从模式数据中推导得到一个自由参数。
若在沉降模型中加入粒子碰并过程，并以云滴谱（cloud droplet spectrum）作为初始谱，则无法评判沉降参数化方案的优劣，因为小粒子的碰并率（coagulation rates）主要由其沉降速度（sedimentation velocity）的选取所主导。但若初始谱中已包含足够数量的雨滴，则采用新方法仍可将参数化误差最高降低50%。