Collaborative Research: Observational and Numerical Modeling Studies of Rain Microphysics

合作研究：雨微物理的观测和数值模拟研究

• 批准号: 1901593
• 负责人: Feng Xu
• 金额: $ 31.87万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901593&HistoricalAwards=false
• 关键词: Collaborative; Research; Observational; Numerical; Modeling

Since 1988, the US National Weather Service has operated a network of around 160 advanced Doppler weather radars for monitoring severe weather and issue warnings, for example, of tornadoes, flash-floods, etc. The last major upgrade of these radars occurred in 2013 involving dual-polarization capability which greatly enhances the ability to detect flash-flood producing storms and extreme rainfall from land-falling hurricanes. This project seeks to improve the measurement accuracy of rainfall by such radars using advanced instruments that measure the properties of individual rain drops such as size, shape, concentration and fall speeds. From knowledge of these properties in different rain intensities and types, the algorithms for radar estimation of rainfall rates can be developed with greater accuracy than possible hitherto. In parallel, numerical models that use detailed microphysics of rain formation and evolution are being compared against radar observations to verify that the physical representation and assumptions in the numerical models are indeed correct. This is difficult because heavy rain at the surface originates from many sources aloft at colder temperatures within the storm and it evolves in a complex manner as the drops reach the surface. Thus, the integration of radar measurements with surface rain properties and numerical models is an essential component of this research which is likely to result in improved accuracy of warnings of flood-producing storms issued by the National Weather Service. The main scientific goal of this project is to obtain a deeper understanding of microphysical processes governing the evolution of drop size distributions (DSDs) using a synergistic combination of dual-polarization radar retrievals of DSD moments and one-dimensional (1D) model-predictions of moments and process rates for well-observed cases by the scanning polarimetric C-band ARMOR radar and the Mobile Integrated Profiling System (MIPS) operated by the University of Alabama at Huntsville (UAH). The simulations are based on two new numerical models, (i) the cloud particle model (CPM) developed at the University of Oklahoma which explicitly accounts for the evolution of all the cloud particles under warm rain microphysical processes, and (ii) a novel Monte-Carlo microphysics model (McSnow) developed by the German Weather Service that simulates the evolution of ice, mixed phase and rain based on the physical properties of "super-particles". Three different instruments will be used to measure and characterize the DSDs over the entire range of sizes from 0.1-8 mm with good accuracy (Meteorological Particle Spectrometer, 2D-video disdrometer, and Precipitation Occurrence Sensor System). Simultaneously, the volume above the instruments will be scanned by the dual-pol ARMOR radar as well as MIPS. The two particle models will be run in 1D and the DSD evolution will be compared against surface measurements and radar profiles to infer the dominant microphysical processes that shape the DSD. The coupling between ice processes above the melting level to rain processes below the melting level to the surface needs to be better understood. McSnow-predicted profiles and slopes of dual-pol variables with height from above the bright-band to the surface will be compared with radar observations to infer the dominant processes as well as rain types. Surface measurements will serve as constraints to the model predictions.One central assumption in nearly all collisional process modeling is that raindrops fall at terminal velocity depending only on the drop mass. However, some recent observations of fall speed distributions show that under turbulent conditions, mm-sized drops can deviate significantly from the Gunn-Kinzer terminal velocity equation which needs to be confirmed. The explicit cloud particle model will be used to predict if there is a significant impact of turbulence-induced fall speed deviations (mean and variance) on collisional processes and subsequently on DSD evolution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自1988年以来，美国国家气象局已经运营了一个由大约160个先进多普勒天气雷达组成的网络，用于监测恶劣天气并发出警报，例如龙卷风，山洪暴发等。该项目旨在提高这种雷达测量降雨量的准确性，使用先进的仪器测量单个雨滴的特性，如大小、形状、浓度和下降速度。从这些属性的知识，在不同的雨强和类型，雷达估计降雨率的算法可以开发出更高的精度比迄今为止可能的。与此同时，正在将使用详细的降雨形成和演变微物理学的数值模型与雷达观测进行比较，以验证数值模型中的物理表示和假设确实正确。这是困难的，因为地面上的大雨来自风暴中温度较低的高空的许多来源，并且随着水滴到达表面，它以复杂的方式演变。因此，雷达测量与地面降雨特性和数值模型的整合是这项研究的一个重要组成部分，这可能会导致国家气象局发布的洪水风暴预警的准确性提高。该项目的主要科学目标是，利用双极化雷达反演的液滴粒径分布矩和一维（1D）模型预测的矩和过程速率，通过扫描极化C波段ARMOR雷达和移动的综合剖面系统（MIPS）对观察良好的情况进行协同组合，更深入地了解控制液滴粒径分布演变的微物理过程。由位于亨茨维尔的亚拉巴马大学（UAH）运营。模拟基于两个新的数值模型，（i）俄克拉荷马州大学开发的云粒子模型（CPM），该模型明确说明了暖雨微物理过程下所有云粒子的演变，以及（ii）德国气象局开发的新型蒙特-卡罗微物理模型（McSnow），该模型模拟了冰的演变，混合相和雨的基础上的物理性质的“超粒子”。将使用三种不同的仪器测量和表征0.1-8 mm整个尺寸范围内的DSD，具有良好的准确性（气象粒子光谱仪，2D视频disdrometer和降水发生传感器系统）。同时，仪器上方的体积将被双极ARMOR雷达和MIPS扫描。这两个粒子模型将在1D中运行，并将DSD演变与表面测量和雷达剖面进行比较，以推断形成DSD的主要微物理过程。需要更好地了解融化水平以上的冰过程与融化水平以下的降雨过程之间的耦合。McSnow预测的从亮带上方到地面高度的双极变量的剖面和斜率将与雷达观测进行比较，以推断主导过程以及雨型。表面测量将作为模型预测的约束条件。几乎所有碰撞过程模拟中的一个核心假设是雨滴的最终速度仅取决于雨滴质量。然而，最近对下落速度分布的一些观察表明，在湍流条件下，毫米大小的液滴可能会明显偏离Gunn-Kinzer终端速度方程，这需要得到证实。显式云粒子模型将被用于预测是否有一个显着的影响，碰撞过程中，并随后对DSD evolution.This奖项的碰撞引起的下降速度偏差（平均值和方差）反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。