Collaborative Research: Characterization of the Two-dimensional/Temporal Mosaic of Drop Size Distributions and Spatial Variability (Structure) in Rain

合作研究：雨中液滴尺寸分布和空间变化（结构）的二维/时间镶嵌特征

• 批准号: 1230240
• 负责人: Michael Larsen
• 金额: $ 32.54万
• 依托单位: College of Charleston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230240&HistoricalAwards=false
• 关键词: Collaborative; Research; Characterization; Two; dimensional

Characteristic scales and structures of precipitation particle distributions have generally been explored over dimensions considerably larger than 1 km, yet for several reasons it is important to characterize their variability on far smaller scales. These include the need to: (1) optimize interpretation of observations made by simple rain gages and disdrometers and the translation of such measurements to much larger scales, as needed for validation of numerical weather forecasts and flood warnings; (2) better understand drop interactions and evolution in a multi-dimensional context rather than just the classical time dependent treatment in the vertical for falling precipitation (as has often been considered adequate for representation of processes including raindrop collision, coalescence and break-up); (3) achieve a more complete description of how radar-emitted microwaves interact with rain; and (4) ultimate connection and perhaps translation of 1-D observations of drop size distributions to larger 2-D domains.The intellectual merit of this project derives from quantification of small-scale precipitation structures via combined use of a 2-D video disdrometer in conjunction with a networked array of optical disdrometers, through which investigators will calculate pair-correlation functions in rain to derive improved measures of any given precipitation field's horizontal geometry. Disdrometer observations (whose horizontal separation may be readily adjusted) will also allow examination a wide range of distances for possible scaling, self-similarity and functional structures of drop size distributions so as to better connect with remote-sensor measurements of precipitation covering large areas. The over-arching objective of this work is to collect and analyze time-series observations of rainfall over horizontal scales ranging from centimeters (as observed using a 2-D video disdrometer) up to several tens to perhaps a hundred meters or more. Broader Impacts will come through potential contributions to improved flood prediction and monitoring as well as better processes relevant to soil erosion, through support of hands-on student involvement in, and through improved observational infrastructure at an undergraduate institution serving underrepresented groups.

通常已经在大于1 km的维度上探索了降水颗粒分布的特征量表和结构，但是由于几个原因，重要的是要在较小的尺度上表征它们的变异性。其中包括以下需要：（1）优化对简单雨量和拆卸仪进行的观察的解释，以及将这种测量结果转换为更大尺度的观察，以验​​证数值天气预测和洪水警告； （2）更好地理解在多维环境中的滴相互作用和进化，而不仅仅是垂直降水降低的经典时间依赖性处理（通常被认为足以代表包括雨滴碰撞，合并和分裂在内的过程的代表）； （3）对雷达发射的微波与雨的相互作用进行更完整的描述； （4）最终连接，也许可能对较大的2D域进行1-d观察结果的转换。该项目的智力优点来自对小规模降水结构的量化，这是通过将2-D视频脱机结合结合结合使用的二-D视频脱水仪与一系列的光学脱离阵列的组合使用，并通过哪些调查人员可以通过哪种降雨来衡量雨水，以促进雨水的范围。 几何学。 径流计的观测值（可以容易调整其水平分离）还将允许检查较宽的距离，以进行缩放，自相似性和跌落尺寸分布的功能结构，从而更好地与覆盖大面积的降水测量值进行更好的连接。 这项工作的整个目标是在水平尺度上收集和分析降雨量的时间序列观察，从厘米（使用2-D视频序列计的观察到）多达几十至一百米或更多。 通过改善洪水预测和监测的潜在贡献以及与土壤侵蚀相关的更好的过程，通过支持动手的学生参与以及改善本科生的观察基础设施，在本科生提供不足的群体的本科机构中，将带来更广泛的影响。

项目成果

Refinements to Data Acquired by 2-Dimensional Video Disdrometers

对二维视频测距仪采集的数据进行改进

• DOI: 10.3390/atmos11080855
• 发表时间: 2020
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Larsen, Michael L.;Blouin, Christopher K.
• 通讯作者: Blouin, Christopher K.