The Effects of Stochastic Rainfall Clustering on Radar Measurements

大气中雨滴和云滴的浓度可以看作是随时间和地点波动的随机变量。 Jameson和Kostinski最近的工作表明，这些浓度的概率分布偏离了适用于纯随机分布的理论泊松定律。 这些偏差是由于液滴倾向于聚集成团或小块而引起的。 这种水滴的聚集对降水的发展以及辐射通过云和降水的传播有影响。 本研究的目的是：（1）继续研究聚类的定量表征;（2）评估聚类对雨滴雷达波散射的影响;（3）确定这些影响对雷达测量降雨率估计的重要性。 除了众所周知的米氏散射或瑞利散射之外，这项工作还将尝试确定集群是否可以解释云和降水的显着布拉格型散射。 该方法结合了（1）测量液滴大小和浓度的各种仪器的数据分析和（2）建立在早期聚类表征工作基础上的理论研究。

The concentrations of raindrops and cloud droplets in the atmosphere may be regarded as random variables that fluctuate with time and location. Recent work by Jameson and Kostinski has shown that these concentrations have probability distributions that deviate from the theoretical Poisson law that applies to purely random distributions. The deviations are caused by the tendency of drops to concentrate in clumps or patches. This clustering of drops has implications for precipitation development and for the propagation of radiation through clouds and precipitation. The objectives of this research are (1) to continue studies on quantitative characterization of clustering; (2) to evaluate the effects of clustering on the scattering of radar waves by raindrops; (3) to determine the significance of these effects on the estimation of rainfall rate from radar measurements. The work will also attempt to determine whether clustering can account for significant Bragg-type scattering by clouds and precipitation in addition to the well-understood Mie or Rayleigh scattering. The approach is a combination of (1) analysis of data from various instruments that measure drop sizes and concentrations and (2) theoretical investigations building on the earlier work on the characterization of clustering.