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（National National Weather Service）经营着大约160个高级多普勒天气雷达网络，以监控恶劣的天气并发出警告，例如，龙卷风，闪光液体等。这些雷达的最后一次重大升级发生在2013年，涉及双极化能力，从而大大提高了Flash Flash Flash Flast Flast-Flood frood frood frood farrood farrood farrood farn form form torme form forme form after torme farrive spretire dermere fall降雨。该项目旨在使用高级仪器（例如尺寸，形状，浓度和降速速度）来提高此类雷达降雨的测量准确性。从了解不同降雨强度和类型中的这些特性的知识，可以比迄今为止可能的降雨速率估算雷达估计的算法。同时，正在比较使用雨水形成和进化的详细微物理学的数值模型与雷达观测值进行比较，以验证数值模型中的物理表示和假设确实是正确的。这很困难，因为表面的大雨源自风暴内较冷的温度下的许多来源，并且随着滴水到达表面而以复杂的方式演变。因此，将雷达测量与表面降雨特性和数值模型的整合是这项研究的重要组成部分，这很可能会提高国家气象局发出的洪水风暴的警告准确性。该项目的主要科学目标是，通过使用双极极化雷达雷达雷达检索的协同组合，对落水量分布的演变（DSD）的演变进行更深入的了解由阿拉巴马大学在亨茨维尔（UAH）运营。 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".将使用三种不同的仪器来测量和表征整个尺寸范围内的DSD，从0.1-8 mm（气象颗粒光谱仪，2D-VIDEO序列机和沉淀传感器系统）的精确度良好（气象颗粒光谱仪，2D-VIDEO）。同时，仪器上方的体积将被双POL装甲雷达和MIPS扫描。两个粒子模型将以1D运行，并将DSD演化与表面测量和雷达曲线进行比较，以推断成塑造DSD的主要微物理过程。需要更好地理解熔融水平以上熔融水平与降雨过程之间的冰过程之间的耦合需要更好地理解。将MCSNOW预测的轮廓和双POL变量的斜率与从明亮带上方到表面的高度的斜率与雷达观测值进行比较，以推断出主导过程和雨水类型。表面测量结果将是对模型预测的约束。几乎所有碰撞过程建模中的一个中心假设是，雨滴仅根据降量质量而下降。但是，最近对降速分布的一些观察结果表明，在湍流条件下，MM大小的下降可能会显着偏离Gunn-Kinzer末端速度方程，这需要确认。明确的云粒子模型将用于预测湍流引起的跌落速度偏差（平均值和方差）是否对碰撞过程以及随后在DSD Evolution上产生了重大影响。该奖项反映了NSF的法定任务，并通过使用该基金会的智力优点和广泛的影响来评估Criteria criteria criteria criteria criteria criiteia均可通过评估来获得NSF的法定任务。