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首页> 外文期刊>Journal of the Atmospheric Sciences >Statistical tools for drop size distributions: Moments and generalized gamma
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Statistical tools for drop size distributions: Moments and generalized gamma

机译:液滴大小分布的统计工具:矩和广义伽玛

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摘要

Several problems associated with drop size distributions are treated. For rainfall rate R or radar reflectivity Z high powers of the drop diameters must be taken into account. This paper suggests methods to deal with the relevant moments and to approximate the distributions by a generalized gamma distribution P(x) = gamma[ u, (x/c)(r)]/ Gamma (u). Retaining the power r as a parameter is the difference to a gamma distribution. It leads to a large flexibility, enabling one to fit the distribution to the moments of interest. The three parameters r, u, and c can be fitted exactly to three moments of the order alpha, 2 alpha, and 3 alpha, respectively, where alpha defines the power of the variable. The expressions for the moments and the transformation to another power a are simple for generalized gamma distributions. A quickly converging algorithm to calculate the parameters r, u, and c is shown. The solutions can be obtained in a spreadsheet or from the internet. The proposed technique is particularly powerful for describing sampling distributions with R or Z from a known drop size distribution. Whether the sample size is a fixed number or whether it varies according to a Poisson distribution makes a difference, which is treated here. However, the processes associated with precipitation and drop size distributions seem to be more complex, leading to larger variations than expected from simple models of random sampling. [References: 18]
机译:处理了与墨滴大小分布相关的几个问题。对于降雨率R或雷达反射率Z,必须考虑液滴直径的高倍。本文提出了处理相关矩并通过广义伽马分布P(x)= gamma [u,(x / c)(r)] / Gamma(u)近似分布的方法。将功率r保留为参数是与伽马分布的差。它带来了很大的灵活性,使您可以将分布调整到感兴趣的时刻。三个参数r,u和c可以分别精确地拟合为alpha,2 alpha和3 alpha阶的三个矩,其中alpha定义变量的幂。对于广义伽玛分布,矩的表达和向另一幂a的转换是简单的。显示了一种用于计算参数r,u和c的快速收敛算法。可以在电子表格中或从Internet获得解决方案。所提出的技术对于根据已知墨滴大小分布描述具有R或Z的采样分布特别有力。样本大小是固定数还是根据泊松分布变化都会有所不同,在此进行处理。但是,与降水量和液滴尺寸分布相关的过程似乎更复杂,导致比随机抽样简单模型所预期的变化更大。 [参考:18]

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