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

0409738 Krajorski这项研究的科学目标是提高我们对降雨的小尺度变化的理解。 这一目标的动机是需要发展一个机械的理解降雨量变化和陆面过程之间的联系。 一个相关的目标是制定程序，从天气雷达和雨量计观测中在小的时间和空间尺度上估计降雨率，即在空间和时间尺度上为陆面过程制定控制方程。 这些尺度与传统上用于雷达降水估计的尺度有很大的不同。 使用分析，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确定所观察到的降雨的变化和缩放特性？ 控制降雨率分布特性的物理过程是什么？ 造成雷达降雨估计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）的降雨尺度结构是什么，它与2公里以上尺度的观测结果是否一致？ 如果在2公里以下的降雨尺度发生变化，什么物理过程是造成尺度中断的原因？ 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的变化，从多细胞雷暴系统将用于解决研究问题。 现场将位于爱荷华州东部（爱荷华州市）、北方密西西比（古德温溪实验流域）和巴尔的摩。 研究人员在这些地点开发了实验计划，将密集的雨量计网络，国家气象局WSR-88 D雷达，雨滴仪（用于雨滴大小分布）和研究雷达的观测结果结合起来。 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。 建议的工作中心的智力价值在空间和时间尺度上的降雨率场的结构，重要的陆地表面水文过程的阐明。 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;以及广泛传播实验和分析结果。 拟议的研究将为学生提供一个特殊的机会，发展水文技术的专业知识，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，特别是根据CUAHSI（水文科学促进大学联盟）的设想。举措 对社会的好处将是通过改进对降雨量的遥感，从而更好地预测和控制水资源系统，并及时发出洪水、滑坡和飓风等自然灾害的警报。

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.