Collaborative Research: Space-Time Variability of Rainfall and its Effects on Rainfall Estimation

这项工作是普林斯顿大学、华盛顿大学和俄克拉荷马大学之间的一项合作研究，得到了美国农业部/美国农业研究协会国家沉积实验室的合作支持。该研究的中心主题是降雨在空间和时间上的变异性特征，它如何依赖于风暴环境，以及这种时空变异性如何影响我们估计地表降雨和降雨动能的能力。在密西西比州北部古德温河研究流域的密集雨量计网络上，将进行非常高的空间（几十米）和时间（几十秒）分辨率的雷达观测。这些详细的观测嵌入了更大规模的NEXRAD WSR-88D雷达观测和风暴环境测量。这项工作需要详细的拉格朗日和基于雷达的风暴细胞分析，它们的演变和运动，以及它们如何导致观测到的空间降雨分布。这些区域尺度降雨的时空分析将与风暴环境的物理特性联系起来。在Goodwin Creek流域尺度上，研究了不同方法（即雨量计、雷达和雷达-雨量计组合）估算降雨率和动能通量在空间和时间上的主要误差来源。特别是，我们试图确定雷达估计与雨量计累积降雨量的差异有多少可以通过结合采样体积差异和亚网格尺度降雨变异性来解释，亚网格尺度降雨变异性与表征大气风暴环境的物理参数有关。我们预计，我们的结果将导致更好地理解如何在空间和时间上以不同分辨率获得的数据进行比较和合并。

9909579BurgesThis work is a collaborative research effort between Princeton University, the University of Washington, and the University of Oklahoma, supported by collaboration with the USDA/ARS National Sedimentation Laboratory. The central theme of the proposed research is on the characterization of the variability of rainfall in space and time, how that depends on the storm environment, and how this space-time variability affects our capability to estimate surface rainfall and rainfall kinetic energy. Radar observations at very high spatial (tens of meters) and temporal (tens of seconds) resolution will be made over the dense rain gauge network of the Goodwin Creek research watershed in northern Mississippi. These detailed observations are embedded with the larger-scale NEXRAD WSR-88D radar observations, and measurement of the storm environment.This work entails detailed Lagrangian, radar-based analyses of storm cells, their evolution and motion, and how that results in the observed spatial rainfall distribution.. These space-time analyses of rainfall at the regional scale will be linked to physical properties of the storm environment. At the Goodwin Creek watershed scale, key error sources of the different approaches (i.e., rain gauge, radar, and radar-gauge combined) to estimate rainfall rate and kinetic energy flux in space and time will be investigated. In particular, we seek to determine how much of the variance of radar-estimated versus rain gauge-accumuluated rainfall amount can be explained by the sampling volume difference in combination with the subgrid-scale rainfall variability, which is linked to physical parameters that characterize the atmospheric storm environment. We anticipate that our results will lead to a better understanding of how data obtained at different resolutions in space and time can be compared and merged.