Combined Observational and Model Investigations of the Z–LWC Relationship in Stratocumulus Clouds
Combined Observational and Model Investigations of the Z–LWC Relationship in Stratocumulus Clouds
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DOI：
10.1175/2007jamc1701.1
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发表时间：
2008-02
影响因子：
3
通讯作者：
A. Khain;M. Pinsky;L. Magaritz;O. Krasnov;H. Russchenberg
中科院分区：
文献类型：
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作者：
A. Khain;M. Pinsky;L. Magaritz;O. Krasnov;H. Russchenberg
现场观测表明，层积云和积云雷达反射率与液态水含量（Z-LWC）关系的复杂性和非唯一性。经验（统计）Z-LWC依赖性的参数在很宽的范围内变化。相应地，检索算法的准确性仍然很低。这种情况是部分相关的事实，即经验算法和参数往往是在没有相应的理解负责形成的Z-LWC图的物理机制。在这项研究中，作者使用一个新的云顶边界层（BL）轨迹集合模式研究了Z-LWC关系的形成过程。在该模型中，整个体积的BL覆盖的拉格朗日包裹平流由一个类似的速度场。时变速度场由湍流模型产生，并服从相关湍流定律。每个拉格朗日块代表“云块模型”，精确描述了气溶胶和液滴的扩散增长蒸发过程以及液滴碰撞。事实上，包裹是彼此相邻的允许一个计算沉降液滴和降水（毛毛雨）的形成。典型的地块大小为50米;地块数量为1840个。该模型计算液滴尺寸分布（DSD）以及它们的矩（例如，气溶胶和液滴浓度、质量含量、雷达反射率）。在模型集成过程中，计算每个地块的Z-LWC关系，以及包括所有地块的散射图。该模型再现原位观察到的类型的Z-LWC关系。结果表明，不同的制度代表云的发展的不同阶段：扩散增长，毛毛雨形成的开始，和大雨阶段，分别。的Z-LWC关系的大的散射被发现是任何毛毛雨云的固有属性。Z-LWC图上的不同区域与位于云中不同级别并具有不同DSD的云体积有关。这一发现允许检索算法的改进。
In situ measurements indicate the complexity and nonunique character of radar reflectivity–liquid water content (Z–LWC) relationships in stratocumulus and cumulus clouds. Parameters of empirical (statistical) Z–LWC dependences vary within a wide range. Respectively, the accuracy of retrieval algorithms remains low. This situation is partially related to the fact that empirical algorithms and parameters are often derived without a corresponding understanding of physical mechanisms responsible for the formation of the Z–LWC diagrams. In this study, the authors investigate the processes of formation of the Z–LWC relationships using a new trajectory ensemble model of the cloud-topped boundary layer (BL). In the model, the entire volume of the BL is covered by Lagrangian parcels advected by a turbulent-like velocity field. The time-dependent velocity field is generated by a turbulent model and obeys the correlation turbulent laws. Each Lagrangian parcel represents the “cloud parcel model” with an accurate description of processes of diffusion growth–evaporation of aerosols and droplets and droplet collisions. The fact that parcels are adjacent to each other allows one to calculate sedimentation of droplets and precipitation (drizzle) formation. The characteristic parcel size is 50 m; the number of parcels is 1840. The model calculates droplet size distributions (DSDs), as well as their moments (e.g., aerosol and drop concentration, mass content, radar reflectivity) in each parcel. In the course of the model integration, Z–LWC relationships are calculated for each parcel, as well as the scattering diagram including all parcels. The model reproduces in situ observed types of the Z–LWC relationships. It is shown that different regimes represent different stages of cloud evolution: diffusion growth, beginning of drizzle formation, and stage of heavy drizzle, respectively. The large scattering of the Z–LWC relationships is found to be an inherent property of any drizzling cloud. Different zones on the Z–LWC diagram are related to cloud volumes located at different levels within a cloud and having different DSD. This finding allows for improvement of retrieval algorithms.