A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics
A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics
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DOI：
10.1175/2007jas2263.1
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发表时间：
2008
影响因子：
3.1
通讯作者：
R. Plant;G. Craig
中科院分区：
文献类型：
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作者：
R. Plant;G. Craig
本文介绍了一种适用于气候模式和数值预报模式的深对流随机参数化方案。理论论据和云解析模型的结果进行了讨论，以激励该计划的形式。在确定性的限制，它往往是一个频谱的卷吸/脱气羽流和类似的其他电流参数化。随机变率描述了大尺度平衡态的局部涨落。烟羽是从概率分布函数（PDF）中随机抽取的，该函数定义了在每个模型网格框内找到给定云基质量通量的烟羽的机会。PDF的归一化由系综平均质量通量给出，并且这是用CAPE闭合方法计算的。每个羽流产生的特点是确定使用的适应从凯恩-弗里奇参数化的羽流模型。在单列版本的统一模式的初步测试验证，该计划是有效的，在产生所需的分布的对流变率，而不会产生不利影响的平均状态。
A stochastic parameterization scheme for deep convection is described, suitable for use in both climate and NWP models. Theoretical arguments and the results of cloud-resolving models are discussed in order to motivate the form of the scheme. In the deterministic limit, it tends to a spectrum of entraining/detraining plumes and is similar to other current parameterizations. The stochastic variability describes the local fluctuations about a large-scale equilibrium state. Plumes are drawn at random from a probability distribution function (PDF) that defines the chance of finding a plume of given cloud-base mass flux within each model grid box. The normalization of the PDF is given by the ensemble-mean mass flux, and this is computed with a CAPE closure method. The characteristics of each plume produced are determined using an adaptation of the plume model from the Kain–Fritsch parameterization. Initial tests in the single-column version of the Unified Model verify that the scheme is effective in producing the desired distributions of convective variability without adversely affecting the mean state.