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Application of the Generalized Gamma Model to Represent the Full Rain Drop Size Distribution Spectra

机译:广义伽马模型的应用代表全雨跌幅分布谱

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摘要

We report on measurements of drop size distributions (DSD) using collocated instruments (a Droplet Measurement Technologies, Inc., Meteorological Particle Spectrometer and a 2D-video disdrometer) from two locations with different rainfall climates (Greeley, Colorado, and Huntsville, Alabama, with measurements from the latter that include the outer rainbands of Hurricane Irma). The combination of the two instruments gives what we term as the "full'' DSD spectra, the shape of which generally cannot be represented by the standard gamma model, but instead requires the additional flexibility of the generalized gamma model, which includes two shape parameters (mu and c). The double-moment normalization of DSDs using the third and fourth moments is used to arrive at the intrinsic shapes of the DSD with two shape parameters that are shown to capture simultaneously the drizzle mode as well as the precipitation mode, together with a "plateau'' region between the two. The estimation of mu and c is done with a global search using nonlinear least squares, and the error residuals are examined to check the sensitivity of the parameters to a preselected, allowed tolerance around the minimum error in the mu, c plane. This leads to a range of plausible fits for a given normalized DSD mainly governed by the c parameter. The stability or invariance of the shape of the normalized DSDs from the two sites is examined, and on average the shapes are similar with some variability at the large normalized diameter end that is explained by the aforementioned range of plausible fits. Heuristic goodness-of-fit methods are described that demonstrate that the generalized gamma model outperforms the standard gamma model with only one shape parameter (mu).
机译:我们报告了使用不同降雨气候的两个地点(Greey,Colorado和Huntsville,Alabama,从后者的测量值包括飓风IRMA的外雨带)。两种仪器的组合给出了我们的术语作为“全”DSD光谱,其形状通常不能由标准伽马模型表示,而是需要推广伽马模型的额外灵活性,其包括两个形状参数(mu和c)。使用第三和第四矩的DSD的双矩标准化用于到达DSD的内在形状,其中两个形状参数显示毛毛雨模式和降水模式,与两者之间的“高原”区域一起。使用非线性最小二乘的全局搜索来完成MU和C的估计,并且检查误差残差以检查参数的灵敏度,以预选的,允许的允许容忍围绕MU,C平面中的最小误差。这导致了一系列合理的适合于主要由C参数控制的给定规范化DSD。研究了来自两个位点的归一化DSD的形状的稳定性或不变性,并且平均在于通过上述范围的合理拟合来解释的大归一化直径端的一些可变性。描述了启发式的拟合方法,表明广义伽马模型仅具有一个形状参数(MU)的标准伽马模型。

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