Observations of the Spatial-Statistical Structures of Precipitation

降水空间统计结构的观测

• 批准号: 0804440
• 负责人: Arthur Jameson
• 金额: $ 44.83万
• 依托单位: RJH Scientific Incorporated
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0804440&HistoricalAwards=false
• 关键词: Observations; Spatial; Statistical; Structures; Precipitation

Radar-derived estimates of rainfall intensity and accumulations offer unsurpassed spatial and temporal resolution, and are thus critical not only for issuance of flash-flood warnings but more broadly as input to agricultural and hydrological models for cropland and river/streamflow management. These measurements are thus a critically important product of the nationwide WSR-88D "NEXRAD" radar network. This research effort is focused on improved rainfall estimates through detection of departures from well-behaved "Rayleigh-type" radar signal behavior that may induce errors in deduced rainfall. The presumption that well-behaved Rayleigh-type statistics dominate observed storm properties is at the foundation of current radar-based precipitation estimation techniques. Though strong spatial gradients of rainfall intensity characteristic of thunderstorms are one potential source of non-Rayleigh signal behavior, research suggests that this complication may also arise in the context of more homogeneous precipitation for certain types of radars and radar scanning strategies (including measurements of differential radar reflectivity and derived hydrometeor type from polarized radars). In extreme cases--again generally associated with the heaviest areas of precipitation--induced errors may locally approach the magnitude of the derived rain rate itself. Within the context of this study, the existence of this statistical complication will be conveyed via computation of a "clustering index (CI)." With the advent of high-resolution weather forecast models, radar observations will likely soon be assimilated into these in real-time to help better guide their predictions. The ability to reduce radar errors (or even simply better quantify the degree of uncertainty inherent in these measurements) would be of particular value in the context of modern data assimilation schemes. This proposal seeks to process high-resolution radar data collected by the NSF-supported CHILL radar in eastern Colorado to better relate the volumetric structure and evolution of CI anomalies to storm morphology and underlying cloud microphysical processes, as well as to extend CI measurements to snow, graupel and hail events. Other potential contributors to radar reflectivity bias (including "Bragg scattering," generally regarded as arising from the turbulent mixing of media with differing indices of refraction, as may occur at cloud/plume edges) will also be examined. The availability of high resolution measurements from CHILL (viz. radial data spacing as fine as 30 m) will further facilitate this work. Subsidiary efforts will address independent data sources such as surface-based disdrometer raindrop size distributions to shed additional light on non-Rayleigh precipitation behavior. The intellectual merit of this effort thus focuses on improved radar signal processing and associated understanding of the structure and dynamics of a wide variety of precipitating clouds. As suggested above, Broader Impacts will include the potential for appreciably improved measurements and short-term forecasts of precipitation intensity and amount.

由雷达得出的降雨强度和累积估计提供了无与伦比的空间和时间分辨率，因此不仅对发布突发洪水警报至关重要，而且更广泛地作为农田和河流/溪流管理的农业和水文模型的投入。因此，这些测量是全国WSR-88D“NEXRAD”雷达网络的一个至关重要的产品。这项研究工作的重点是通过检测偏离良好的“瑞利型”雷达信号行为来改进降雨量估计，这些信号行为可能会导致推断降雨量的误差。良好的瑞利型统计量主导观测到的风暴特性的假设是当前基于雷达的降水估计技术的基础。虽然雷暴特征的强降雨强度空间梯度是非瑞利信号行为的一个潜在来源，但研究表明，对于某些类型的雷达和雷达扫描策略（包括差分雷达反射率的测量和从极化雷达导出的水流星类型）来说，这种复杂性也可能出现在更均匀降水的背景下。在极端情况下——同样通常与降水最严重的地区有关——引起的误差可能在局部接近推导出的降雨率本身的大小。在本研究的背景下，这种统计复杂性的存在将通过计算“聚类指数（CI）”来传达。随着高分辨率天气预报模型的出现，雷达观测可能很快就会被实时吸收，以帮助更好地指导他们的预测。在现代数据同化方案的背景下，减少雷达误差（或甚至只是更好地量化这些测量中固有的不确定性程度）的能力将具有特别的价值。该提案旨在处理由美国国家科学基金会支持的科罗拉多州东部的CHILL雷达收集的高分辨率雷达数据，以更好地将CI异常的体积结构和演变与风暴形态和底层云微物理过程联系起来，并将CI测量扩展到雪、霰和冰雹事件。雷达反射率偏差的其他潜在因素（包括“布拉格散射”，通常被认为是由具有不同折射率的介质的湍流混合引起的，如可能发生在云/羽流边缘）也将进行研究。从CHILL获得的高分辨率测量（即径向数据间距精确到30米）将进一步促进这项工作。辅助工作将处理独立数据源，如基于地表的分差仪雨滴大小分布，以进一步阐明非瑞利降水行为。因此，这项工作的智力价值集中在改进雷达信号处理和对各种降水云的结构和动力学的相关理解上。如上所述，更广泛的影响将包括显著改进降水强度和数量的测量和短期预报的潜力。