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视为随时间和位置波动的随机变量。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。偏差是由液滴聚集成块或片状的趋势引起的。水滴的这种聚集性使区分降雨率和雨滴大小分布等量的实际时间和空间变异性与包含少量水滴的样本自然产生的变异性之间的问题复杂化。该项目的主要目标是制定新的采样标准，用于计数水滴和估计其浓度，以确定真实的气象可变性，并避免因小样本而产生的伪影。另一个目标是设计新的数据分析技术，对结构成团的云进行采样，并描述其结构。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)对测量液滴大小和浓度的各种仪器的数据进行分析和(2)基于蒙特卡罗模拟的理论研究。这项工作将通过定义测量和描述雨和云的时空结构的准确方法，从根本上为云物理学做出贡献。

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.