科罗拉多州立大学区域大气模拟系统(RAMS)中云滴的大液滴模式和预测数浓度。第二部分:对科罗拉多州冬季降雪事件的敏感性

S. Saleeby, W. Cotton
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引用次数: 48

摘要

本文是描述科罗拉多州立大学区域大气模拟系统(RAMS)微物理模块新增加的两部分系列中的第二部分。这些变化包括在液滴光谱中加入了直径40-80 μm的大云滴模式,以及通过激活云凝结核(CCN)和巨云凝结核(GCCN)来参数化云滴成核。为了更精确地表示云滴分布的自然双模态,引入了大液滴模态。参数化的液滴成核取代了以前仅通过过饱和计算对云液滴形成的估计。在本系列的第一部分中,详细介绍了微物理的改进,包括用于参数化这一复杂过程的拉格朗日包裹模型中控制云滴发展的一组方程。Supercell的模拟是根据模型感知进行测试的……
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Large-Droplet Mode and Prognostic Number Concentration of Cloud Droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part II: Sensitivity to a Colorado Winter Snowfall Event
Abstract This paper is the second in a two-part series describing recent additions to the microphysics module of the Regional Atmospheric Modeling System (RAMS) at Colorado State University. These changes include the addition of a large-cloud-droplet mode (40–80 μm in diameter) into the liquid-droplet spectrum and the parameterization of cloud-droplet nucleation through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was introduced to represent more precisely the natural dual mode of the cloud-droplet distribution. The parameterized droplet nucleation replaces the former estimation of cloud-droplet formation solely from supersaturation calculations. In Part I of this series, details of the improvements to the microphysics were presented, including the set of equations governing the development of cloud droplets in the Lagrangian parcel model that was employed to parameterize this complex process. Supercell simulations were examined with respect to the model sensi...
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