We generated a large number 105,000 of aggregates composed of various monomer types and sizes using an aggregation model. Combined with hydrodynamic theory, we derived ice particle properties such as mass, projected area, and terminal velocity as a function of monomer number and size. This particle ensemble allows us to study the relation of particle properties with a high level of detail which is often not provided by in situ measurements. The ice particle properties change rather smoothly with monomer number. We find very little differences in all particle properties between monomers and aggregates at sizes below 1 mm which is in contrast to many microphysics schemes. The impact of the monomer type on the particle properties decreases with increasing monomer number. Whether, for example, the terminal velocity of an aggregate is larger or smaller than an equal‐size monomer depends mostly on the monomer type. We fitted commonly used power laws as well as Atlas‐type relations, which represent the saturation of the terminal velocity at large sizes (terminal velocity asymptotically approaching a limiting value) to the data set and tested the impact of incorporating different levels of complexity with idealized simulations using a 1D Lagrangian super particle model. These simulations indicate that it is sufficient to represent the monomer number dependency of ice particle properties with only two categories (monomers and aggregates). The incorporation of the saturation velocity at larger sizes is found to be important to avoid an overestimation of self‐aggregation of larger snowflakes.

中文翻译：

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凝集模拟推断冰粒性质

我们使用聚集模型生成了105,000个由各种单体类型和大小组成的聚集体。结合流体力学理论，我们得出了冰粒的性质，例如质量，投影面积和最终速度，这是单体数量和大小的函数。这种粒子集合使我们能够以较高的详细程度研究粒子属性之间的关系，而这些细节通常无法通过原位测量获得。冰粒性质随单体数变化相当平稳。我们发现，尺寸小于1毫米的单体和聚集体之间，所有颗粒性质的差异都很小，这与许多微物理方案相反。单体类型对颗粒性质的影响随单体数量的增加而降低。例如，是否 聚集体的最终速度大于或小于同等大小的单体主要取决于单体类型。我们将常用的幂定律以及阿特拉斯类型关系拟合到数据集，这些关系代表大尺寸时的终端速度饱和度（终端速度渐近接近极限值），并测试了将不同层次的复杂性与理想化相结合的影响使用一维拉格朗日超级粒子模型进行模拟。这些模拟表明，仅用两种类别（单体和聚集体）就足以表示冰粒特性的单体数量依赖性。发现在较大尺寸下并入饱和速度对于避免高估较大雪花的自聚集很重要。