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，首席研究人员将开发一种新方法，使用极化雷达来衡量降雨。 从极化天气雷达获得的最新证据表明，当现有的极化方法无法充分估计降雨量时，滴量尺寸分布的可变性（DSD）是一个重大原因。 因此，了解DSD对降雨估算的偏振算法的性能的影响将是这项研究工作的重大推动力。 主要研究人员将利用这一知识来开发一种新颖的降雨估计方法。 这个想法是使用偏光雷达数据根据其DSD对各种降雨制度进行分类，以便可以将适当的极化关系与每种雨类型持续匹配。 降雨状态的表征将来自各种极化变量。 相应的DSD将通过最先进的二维视频示意图来衡量。 拆卸计将放置在一个紧密间隔的雨量仪表内的坑中，以消除风能并获得对降雨的独立估计。 隔离计数据将在俄克拉荷马州风暴中发现的所有类型的降雨中收集，以发展降雨制度并了解各种制度之间的差异。与微片的降雨量计数据相比，“匹配”的降雨量估计值将完成，以相比之下Mesonet（每个俄克拉荷马州县的一个或两个仪表）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计质量的影响。在长距离上，降雨估计值的不确定性增加了降水的非均匀雷达梁填充和垂直分层。网络雨量测量值的补充，俄克拉荷马城以西的西马隆极化雷达，俄克拉荷马州中部及其周边地区的三个操作WSR-88D雷达以及诺曼的雨量规比较设施提供了一个独特的现场实验室，以支持调查。 此外，主要研究人员的雷达 - 雨量表比较的历史悠久。雷达降雨估计的改善将对降水预测的质量产生积极影响。 改进的估计值将由于洪水泛滥而有助于减轻自然灾害，并通过政府和商业组织更好地管理水资源。