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

解决云雨中的空间分辨率和充分采样问题

• 批准号: 0242418
• 负责人: Arthur Jameson
• 金额: $ 24万
• 依托单位: RJH Scientific Incorporated
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0242418&HistoricalAwards=false
• 关键词: Addressing; Problems; Spatial; Resolution; Adequate

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.

大气中雨滴和云滴的浓度可以被视为随时间和地点波动的随机变量。 Jameson 和 Kostinski 最近的研究表明，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。 这些偏差是由液滴集中成团或斑块的倾向引起的。 液滴的这种聚集使区分降雨率和液滴尺寸分布等量的实际时间和空间变化以及包含少量液滴的样本自然产生的变化的问题变得复杂。 该项目的主要目标是制定新的采样标准来计数液滴并估计其浓度，从而识别真实的气象变化并避免小样本产生的伪影。 另一个目标是设计新的数据分析技术来对结构密集的云进行采样并表征其结构。 还将关注制定抽样标准以正确描述液滴尺寸分布。 该方法结合了 (1) 对测量液滴尺寸和浓度的各种仪器的数据进行分析，以及 (2) 基于蒙特卡罗模拟的理论研究。 这项工作将从根本上为云物理学做出贡献，定义测量和描述雨和云的时空结构的准确方法。