Collaborative Research: Small-Scale Variability of Rainfall: Experimental Studies with Implications for Rainfall Estimation

这项研究的科学目标是提高我们对降雨小尺度变化的理解。这一目标的动机是需要对降雨变率与陆地表面过程之间的联系发展一种机制上的理解。一个相关的目标是发展在小时间和空间尺度上根据气象雷达和雨量计观测估计降雨率的程序，即在空间和时间尺度上为陆地表面过程制定控制方程。这些尺度与传统上用于雷达降雨估计的尺度有很大不同。利用分析、计算和观测工具，该团队提出解决以下科学问题：风暴结构、风暴运动和风暴演变如何决定观测到的降雨变异性和尺度特性？控制降雨率分布特性的物理过程是什么？造成雷达雨量估测不确定性的主要物理过程是什么？2公里以下尺度（传统雷达降雨估计的典型空间尺度）的降雨尺度结构是怎样的？它与大于2公里尺度的观测结果是否一致？如果在2公里以下有降雨尺度的变化，是什么物理过程导致了尺度的中断？该小组将通过实地测量、数据分析和统计建模的协调计划来研究这些问题。拉格朗日和欧拉表示的降雨变异性从多细胞雷暴系统将用于解决研究问题。现场地点将位于爱荷华州东部（爱荷华市），密西西比州北部（古德温河实验流域）和巴尔的摩。研究人员在这些地点开发了实验计划，包括密集雨量计网络、国家气象局WSR-88D雷达、分流仪（用于雨滴大小分布）和研究雷达的观测结果。这些观测资源将通过在爱荷华州和巴尔的摩的实地活动得到加强。所提出的工作的智力优点集中在阐明对地表水文过程重要的时空尺度上的降雨率场结构。更广泛的影响包括在促进教学、培训和学习的同时推进发现；为代表性不足的群体提供研究机会，加强研究和教育的基础设施；广泛传播实验和分析结果。拟议的研究将为学生提供一个发展水文技术专业知识的绝佳机会，特别是通过参与实地实验。拟议的工作还将为水文实验所需的测量技术提供重要的见解，特别是在CUAHSI（促进水文科学大学联盟，Inc.）倡议下的设想。对社会的好处将是通过改善对降雨的遥感，这将导致更好的预测和控制水资源系统，以及对洪水、山体滑坡和飓风等自然灾害的及时预警。

0409738KrajewskiThe scientific goal of this study is to improve our understanding of the small-scale variability of rainfall. This goal is motivated by the need to develop a mechanistic understanding of the linkage between rainfall variability and land surface processes. A related goal is development of procedures for estimating rainfall rate from weather radar and rain gage observations at small time and space scales, i.e. the spatial and temporal scales for which the governing equations for land surface processes are developed. These scales are quite different from those that have been conventionally been employed for radar rainfall estimation. Using analytical, computational, and observational tools, the team proposes to address the following scientific questions: How do storm structure, storm motion, and storm evolution determine the observed variability and scaling properties of rainfall? What are the physical processes that control the distributional properties of rainfall rate? What are the dominant physical processes that are responsible for uncertainty in radar-rainfall estimates? What is the scaling structure of rainfall at scales below 2 km (a typical spatial scale for conventional radar rainfall estimates) and is it consistent with that observed at scales greater than 2 km? If there are changes in rainfall scaling below 2 km, what physical processes are responsible for breaks in the scaling? The group will examine these questions through a coordinated program of field measurements, data analysis, and statistical modeling. Lagrangian and Eulerian representations of variability of rainfall from multicell thunderstorm systems will be utilized for addressing the research questions. The field sites will be located in eastern Iowa (Iowa City), northern Mississippi (Goodwin Creek experimental watershed) and Baltimore. The investigators have developed experimental programs at these locations that incorporate observations from dense rain gage networks, the National Weather Service WSR-88D radars, disdrometers (for raindrop size distributions), and research radars. These observational resources will be enhanced through field campaigns in Iowa and Baltimore. The intellectual merit of the proposed work centers on elucidating the structure of rainfall rate fields at spatial and temporal scales important to land surface hydrologic processes. The broader impacts include advancing discovery while promoting teaching, training and learning; providing research opportunities for underrepresented groups, enhancing the infrastructure for research and education; and broad dissemination of experimental and analytical results. The proposed research will provide students with an exceptional opportunity to develop expertise in hydrologic technology, especially through participation in field experiments. The proposed work will also provide important insights to the measurement technology needed for hydrologic experimentation, especially as envisioned under the CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.) initiatives. The benefits for society will be through improvements in remote sensing of rainfall, which will lead to better prediction and control of water resources systems, and timely warnings against natural hazards such as floods, landslides, and hurricanes.