Collaborative Research to Explore the Spatial/Temporal Statistical-Physical Structures of Rain in the Vertical Plane

在时间和空间上充分了解降雨对于提高公共安全和生产力非常重要。在公共安全方面，这意味着更好的水控制设计，从较小的城市到较大的区域范围减少洪水的影响，并改善洪水的时空警报，以减少损失和死亡。关于农村地区的生产力，这类信息有可能通过更好地设计农田径流和蓄水来减少土壤侵蚀的影响。在更多的城市地区，生产力部分取决于良好的无线通信。然而，5G无线网络的出现将更容易受到降雨造成的信号损失的影响，这种损失也取决于降雨的空间结构，特别是在地面上。然而，所有这些改进都取决于雷达测量和数值模型输出到许多不同尺度的适当转换。这反过来又要求将地面以上的雷达测量结果以及数值模型产品正确地转移到人们生活的地表上。这就是本项目的中心目标。更具体地说，在科学方面，本项目允许在多维背景下（包括高度）对雨结构进行更完整的描述，并有助于更好地多维理解水滴相互作用和演变。此外，该项目旨在更全面地调查统计同质性在所有三个空间维度以及时间上的普遍性和重要性。统计均匀性对于降雨测量的适当缩放是重要的，但是，当然，并非所有的降雨都是统计均匀的。然而，当数据在统计学上是不均匀的时，通常会出现这样的情况：这些数据可以被细分为统计学上均匀的部分，然后可以适当地进行分析处理。本项目发展了这种方法。此外，风塔测量以及两个2DVD disdrometers之间的同步测量将被用来进一步探索下降下降速度从他们的名义终端值在高处和在表面上的偏差报告。这对正确理解降雨的演变可能非常重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A good understanding of rain in time and space is important for improved public safety and productivity. With regard to public safety, this means better water control designs for reducing the impact of flooding from smaller urban to larger regional scales as well as for improved spatial and temporal warnings of flooding in order to reduce damage and fatalities. With regard to productivity in rural areas, such information has the potential for reducing the impacts of soil erosion through the better design of field run-off and water storage. In more urban areas, productivity depends in part upon good wireless communication. However, the advent of 5G wireless networks will be more subject to signal loss due to rain, a loss that also depends upon the largely unknown spatial structure of rainfall particularly above the ground. All of these improvements, however, depend upon the proper translation of radar measurements and numerical model outputs to many different scales. This in turn requires that radar measurements above the ground as well as numerical model products be correctly transferred to what appears at the surface where people live. This, then, is a central objective of this project.More specifically with regard to the science, this project allows a more complete description of rain structure in a multi-dimensional context including height with the additional benefit of developing a better multi-dimensional understanding of drop interactions and evolution. Furthermore, the project is directed toward a more comprehensive investigation into the prevalence and the importance of statistical homogeneity in all three spatial dimensions as well as in time. Statistical homogeneity is important for the proper scaling of rainfall measurements, but, of course, not all rain is statistically homogeneous. However, it often appears that when the data are statistically heterogeneous, such data may be subdivided into statistically homogeneous sections which can then be properly treated analytically. This project develops this approach. In addition, wind tower measurements as well as simultaneous measurements between two 2DVD disdrometers will be used to explore further the reported deviations of drop fall speeds from their nominal terminal values both aloft and at the surface. This is potentially quite important for the proper understanding of the evolution of rain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.