Rainfall Estimation Using Palarimetric Radar

使用参数雷达估算降雨量

• 批准号: 9907930
• 负责人: Alexander Ryzhkov
• 金额: $ 29.39万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9907930&HistoricalAwards=false
• 关键词: Rainfall; Estimation; Using; Palarimetric; Radar

Under support from the U.S. Weather Research Program, the Principal Investigators will develop a new method to measure rainfall using polarimetric radar. Recent evidence obtained from polarimetric weather radars suggests that when existing polarimetric methods fail to adequately estimate rainfall, a significant cause is the variability in drop size distribution (DSD). Thus, understanding the impact of DSD on the performance of the polarimetric algorithms for rainfall estimation will be a major thrust of this research effort. The Principal Investigators will exploit this knowledge to develop a novel approach for rainfall estimation. The idea is to use polarimetric radar data to classify various rainfall regimes according to their DSD so that appropriate polarimetric relations can be continuously matched to each rain type. Characterization of a rainfall regime will be derived from various polarimetric variables. The corresponding DSD's will be measured by a state-of-the-art two-dimensional video-disdrometer. The disdrometer will be placed in a pit within an array of closely spaced rain gauges to eliminate wind effects and to acquire independent estimates of rainfall. Disdrometer data will be collected in all types of rainfall found in Oklahoma storms to develop the rain regime classification and to understand the differences among the various regimes.Verification of the "matched" polarimetric rainfall estimates will be done by comparison with rain gauge data from a micronetwork (42 gages at 5 km spacing, operated by the Agricultural Research Service, USDA) in south-central Oklahoma, the Oklahoma Mesonet (one or two gauges in each Oklahoma county), and the NOAA Cooperative Network. The latter two data sets will be employed primarily to assess range effects on the quality of radar rainfall estimation. At long distances non-uniform radar beam filling and vertical stratification of precipitation add to the uncertainty in rainfall estimates. The complement of network rain gauges, the Cimarron polarimetric radar just west of Oklahoma City, three operational WSR-88D radars in and around central Oklahoma, and a rain gauge comparison facility in Norman, provide a unique field laboratory to support the investigation. In addition, the Principal Investigators have a long history of radar - rain gauge comparisons.Improvement in radar rainfall estimation will have a positive impact on the quality of precipitation forecasts. Improved estimates will assist in mitigation of natural disasters due to flooding and lead to better management of water resources by government and commercial organizations.

在美国天气研究计划的支持下，主要研究人员将开发一种使用极化雷达测量降雨量的新方法。 最近从偏振天气雷达获得的证据表明，当现有的偏振方法无法充分估计降雨量时，一个重要原因是雨滴尺寸分布（DSD）的变化。 因此，了解 DSD 对降雨估计极化算法性能的影响将是这项研究工作的主要推动力。 首席研究员将利用这些知识开发一种新的降雨估算方法。 这个想法是使用极化雷达数据根据 DSD 对各种降雨状况进行分类，以便可以将适当的极化关系连续匹配到每种降雨类型。 降雨状况的特征将从各种极化变量中得出。 相应的 DSD 将通过最先进的二维视频测距仪进行测量。 测速仪将被放置在一系列紧密排列的雨量计内的坑中，以消除风的影响并获得对降雨量的独立估计。 将收集俄克拉荷马州风暴中发现的所有类型降雨的测速仪数据，以制定雨情分类并了解不同雨情之间的差异。将通过与来自一个雨量计的雨量计数据进行比较来验证“匹配”的极化降雨估计。俄克拉荷马州中南部的微网络（间隔 5 公里的 42 个测量仪，由美国农业部农业研究局运营）、俄克拉荷马州 Mesonet（俄克拉荷马州每个县一到两个测量仪）和 NOAA合作网络。 后两个数据集将主要用于评估范围对雷达降雨估算质量的影响。在长距离处，不均匀的雷达波束填充和降水的垂直分层增加了降雨估计的不确定性。网络雨量计的补充、俄克拉荷马城以西的 Cimarron 偏振雷达、俄克拉荷马州中部及周边地区的三台运行中的 WSR-88D 雷达以及诺曼的雨量计比较设施，提供了一个独特的现场实验室来支持调查。 此外，主要研究人员在雷达-雨量计比较方面有着悠久的历史。雷达降雨量估算的改进将对降水预报的质量产生积极影响。 改进的估计将有助于减轻洪水造成的自然灾害，并促使政府和商业组织更好地管理水资源。