Addressing the Problems of Spatial Resolution and Adequate Sampling in Clustered Clouds and Rain

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。 这种液滴的聚集使得区分诸如降雨率和液滴大小分布等量的实际时间和空间变异性与包含少量液滴的样本自然产生的变异性的问题变得复杂。 该项目的主要目标是制定新的采样标准，用于对水滴进行计数并估计其浓度，以确定真实的气象变化，避免小样本造成的人为现象。 另一个目标是设计新的数据分析技术，以采样的云是clustralian结构和表征其结构。 还将注意制定适当描述液滴大小分布的抽样标准。 该方法结合了（1）测量液滴尺寸和浓度的各种仪器的数据分析和（2）基于Monte Carlo模拟的理论研究。 这项工作将通过定义测量和描述雨和云的空间和时间结构的准确方法，从根本上促进云物理学。

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 complicates the problem of distinguishing between actual time and space variability of quantities such as the rainfall rate and the drop-size distribution, and the variability arising naturally from samples containing small numbers of drops. The main objective of this project is to develop new sampling criteria for counting drops and estimating their concentration that will identify real meteorological variability and avoid the artifacts arising from small samples. Another objective is to devise new data analysis techniques to sample clouds that are clumpy in structure and to characterize their structure. Attention will also be given to developing sampling criteria for the proper description of drop-size distributions. The approach is a combination of (1) analysis of data from various instruments that measure drop sizes and concentrations and (2) theoretical investigations based on Monte Carlo simulation. The work will contribute fundamentally to cloud physics by defining accurate methods for measuring and describing the spatial and temporal structure of rain and clouds.