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何马大学之间的合作研究成果，得到了美国农业部/ARS国家沉积实验室的支持。拟议研究的中心主题是关于降雨在空间和时间上的变异性的特征，这如何取决于风暴环境，以及这种时空变异性如何影响我们估计地表降雨量和降雨动能的能力。将在密西西比州北部古德温溪研究分水岭的密集雨量计网络上进行非常高空间(数十米)和时间(数十秒)分辨率的雷达观测。这些详细的观测与更大规模的NEXRAD WSR-88D雷达观测和风暴环境的测量相结合。这项工作需要对风暴单体、它们的演变和运动以及如何导致观测到的降雨量空间分布进行详细的拉格朗日分析。这些区域尺度的降雨量时空分析将与风暴环境的物理特性联系起来。在Goodwin Creek分水岭尺度上，将调查不同方法(即雨量计、雷达和雷达计组合)在空间和时间上估计降雨率和动能通量的主要误差源。特别是，我们试图确定雷达估计的与雨量计累积的降雨量之间的差异在多大程度上可以通过采样体积差与次网格尺度的降雨变率相结合来解释，次网格尺度的降雨量变率与表征大气风暴环境的物理参数有关。我们预计，我们的结果将有助于更好地理解如何比较和合并以不同空间和时间分辨率获得的数据。

0002019Wurman This 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.