Radar-Derived Refractivity and Its Impact on Model Analysis and Forecasting Using Advanced Data Assimilation Methods

地球表面附近大气湿度的精细尺度变化被认为是各种天气现象的熟练预报的关键，最值得注意的是严重雷暴的开始和随后的演变。虽然由自动气象站进行的相对密集的直接地面观测网络具有相当大的价值，但成本和其他实际考虑使其无法在足够细的网格上实施，从而无法捕捉准确预测雷暴发展的时间和位置所需的关键湿度变化。同样，虽然通过卫星搭载的基于GPS的技术遥感大气水蒸气的新兴手段有望在更大尺度上进行热力学分析，但它们缺乏这种详细应用所需的分辨率。因此，地面遥感技术被认为是最有前途的方法，用于测绘水平尺度10公里的水分变化，最近的工作表明，运行中的多普勒雷达可能有能力可靠地测量近地表的大气活动场（其涉及雷达信号通过大气介质的传播速度），其又强烈地依赖于大气水分含量。只有当这些信号是由任何给定雷达周围的静止地面目标的反射（或更准确地说，反向散射）产生时，才能有意义地解释这些信号。这里所支持的工作集中在对这种主动性技术的系统探索和评估上，这种主动性技术应用于各种作战地基雷达平台--既包括目前正在使用的（如WSR-88 D/NEXRAD系统），也包括正在设计中的（如NSF支持的CASA蜂窝塔安装原型网络）。本研究旨在开发一种灵活而高效的“雷达活动引擎”，可用于可靠地映射可能部署在任何给定区域的各种雷达平台的雷达活动变化。计划在2008-2010年强风暴季节使用位于俄克拉荷马州战略位置的七个多普勒雷达进行实验。还将开发先进的雷达模拟技术，以进行统计误差分析，并确定实际雷达活动测量值有效代表的有效分辨率（即测量网格的精细度）。为了评估这些信息的气象影响，研究人员将制定必要的框架，以最佳地纳入（又名同化）的活动数据作为数值预报模型的输入。利用模拟和真实的观测，将进行一项系统的研究，以评估各种/选定的平台上的雷暴活动测量对雷暴发生预报的影响，以及预先存在的风暴的演变和定量降水预报的最终准确性。这项工作产生的更广泛的影响将包括开发软件/可能适用于在美国各地运行的多普勒雷达网络的硬件工具，并最终有助于改善对强风暴和降水的预报。这项工作还将支持培训新一代的学生，如先进的雷达信号处理，数据同化和风暴的数值模拟。

Fine-scale variations in atmospheric moisture near the earth's surface are thought to be pivotal to skillful forecasts of a variety of weather phenomena, most notably the initiation and subsequent evolution of severe thunderstorms. While networks of relatively dense direct surface observations by automated weather stations are of considerable value, cost and other practical considerations prevent their implementation on a sufficiently fine mesh so as to capture key moisture variations required to accurately anticipate the timing and location of thunderstorm development. Likewise, while emerging means to remotely sense atmospheric water vapor via satellite borne GPS-based technology hold promise for thermodynamic profiling at larger scales, they lack the resolution required for such detailed applications. Hence ground-based remote sensing techniques are viewed as a the most promising approach for mapping moisture variations on horizontal scales 10 km, even in quiescent pre-storm environments.Recent work has suggested that operational Doppler radars may have the ability to reliably measure the near-surface refractivity field (which relates to the propagation speed of radar signals through the atmospheric medium) that is in turn strongly dependent on atmospheric moisture content. These signals can be meaningfully interpreted only when they result from reflection (or more accurately, backscatter) by stationary ground targets immediately surrounding any given radar. The work supported here centers on a systematic exploration and evaluation of this refractivity technique as applied to a variety of operational ground-based radar platforms--both those presently in use (such as the WSR-88D/NEXRAD system) and on the drawing board (such as the NSF-supported CASA celltower-mounted prototype network). This study seeks to develop a flexible yet efficient "refractivity engine" that can be used to reliably map refractivity changes from a variety of radar platforms likely to be deployed over any given region. Experiments are planned for the 2008-2010 severe storm seasons using seven strategically located Doppler radars in Oklahoma. Advanced radar simulation techniques will also be developed to conduct statistical error analyses and determine the effective resolution (i.e. fineness of the resulting measurement mesh) effectively represented by actual radar refractivity measurements. In order to assess the meteorological impact of this information, the investigators will develop the framework necessary for optimal inclusion (aka assimilation) of refractivity data as input to numerical forecast models. Using both simulated and real observations, a systematic study will be conducted to assess the impact of refractivity measurements from various/selected platforms on forecasts of thunderstorm initiation, as well as the evolution of pre-existing storms and ultimate accuracy of quantitative precipitation forecasts.Broader impacts emerging from this work will include development of software/hardware tools potentially applicable to Doppler radar networks operating across the U.S., and could ultimately contribute to improved forecasting of severe storms and precipitation. This work will also support training a new generation of students in subjects such as advanced radar signal processing, data assimilation, and numerical modeling of storms.