A Cloud Physical Parameterization Method Using Movable Basis Functions: Stochastic Coalescence Parcel Calculations
A Cloud Physical Parameterization Method Using Movable Basis Functions: Stochastic Coalescence Parcel Calculations
复制标题
使用可动基函数的云物理参数化方法：随机合并地块计算
DOI：
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发表时间：
1983
期刊：
影响因子：
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通讯作者：
W. Hall
中科院分区：
文献类型：
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作者：
T. Clark;W. Hall
使用一系列分布函数，对气块框架内的随机碰并进行了数值模拟。利用带约束的变分方法推导了控制分布参数趋势的方程。将具有两个和三个对数正态分布函数的解与常规基准模型进行了比较，结果表明分布模型能得出准确的解。尽管本文仅考虑碰并，但也讨论了纳入其他物理过程的步骤。文中所有模拟均使用对数正态分布，不过该方法具有足够的通用性，可适用于其他分布，如伽马分布。因变量数量减少多达10倍，以及处理碰并方程所需的计算时间相应减少，使得分布模型对多维云模式模拟具有吸引力……
Abstract Numerical simulations of stochastic coalescence in a parcel framework are presented using a series of distribution functions. The equations governing the distribution parameter tendencies are derived using a variational approach with constraints. Solutions with two and three log-normal distribution functions are compared with a conventional benchmark model and the distribution model is shown to produce accurate solutions. Although only coalescence is considered within this paper, the procedures for including further physical processes is discussed. All of the simulations presented use the log-normal distribution although the method is general enough that it could be adapted to use other distributions such as the gamma distribution. A decrease in the number of dependent variables by as much as by a factor of 10 as well as an equivalent reduction in computation time required for the treatment of the coalescence equation makes the distribution model attractive for multi-dimensional cloud model simul...