Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.

中文翻译：

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使用多频多普勒雷达观测的统计数据改进两时刻微物理方案中聚集的表示

聚集是形成可沉淀冰粒的关键微物理过程。它的理论描述涉及许多参数和不同变量之间的依赖关系，这些参数在大宗微物理方案中要么没有被充分理解，要么难以准确表示。先前的研究已经证明了多频多普勒雷达观测的有价值的信息内容，可以根据温度等环境参数来表征聚合。与模型模拟的比较可以揭示差异，但主要挑战是确定聚合参数化中最关键的参数，然后可以通过使用观察作为约束来改进这些参数。在这项研究中，我们系统地研究了物理变量的敏感性，例如数量和质量密度，以及前向模拟的多频和多普勒雷达观测值，以两时刻微物理方案中的不同参数。我们的方法包括修改关键聚合参数，例如粘附效率或尺寸分布的形状。我们还修改和测试了改变函数关系（例如，终端速度-大小关系）和基本假设（例如，聚合内核的定义）的影响。我们首先在单列“雪井”模型中测试各种组件的灵敏度，这样可以快速有效地识别与观察结果最佳匹配的参数组合。我们发现粒子特性、聚集核的定义和尺寸分布宽度被证明是最重要的，而黏附效率和云冰习性影响较小。然后使用相同的方案设置在 3D 模型中实现与观察结果最佳匹配的设置。使用新方案设置重新运行 3D 模型数周后，可以显着降低模型中对聚合尺寸和终端速度的大幅高估。所提出的方法有望适用于约束其他冰微物理过程或评估和改进其他方案。使用新方案设置重新运行 3D 模型数周后，可以显着降低模型中对聚合尺寸和终端速度的大幅高估。所提出的方法有望适用于约束其他冰微物理过程或评估和改进其他方案。使用新方案设置重新运行 3D 模型数周后，可以显着降低模型中对聚合尺寸和终端速度的大幅高估。所提出的方法有望适用于约束其他冰微物理过程或评估和改进其他方案。