Collaborative Research: Observational and Numerical Modeling Studies of Rain Microphysics

自1988年以来，美国国家气象局已经运营了一个由大约160台先进的多普勒天气雷达组成的网络，用于监测恶劣天气并发布警报，例如龙卷风、山洪暴发等。这些雷达上一次重大升级是在2013年，涉及双极化能力，大大提高了探测山洪暴发风暴和登陆飓风造成的极端降雨的能力。该项目旨在通过使用先进的仪器来提高这种雷达测量降雨量的精度，这些仪器可以测量单个雨滴的大小、形状、浓度和降落速度等特性。根据对不同降雨强度和类型的这些特性的了解，雷达估计降雨率的算法可以比目前为止更准确地开发出来。同时，使用降雨形成和演变的详细微观物理的数值模型正在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是正确的。这是很困难的，因为地表的暴雨来自于风暴中温度较低的高空的许多来源，随着雨滴到达地表，暴雨以一种复杂的方式演变。因此，将雷达测量与地面降雨特性和数值模式相结合是这项研究的一个重要组成部分，这可能会提高国家气象局发布的洪水风暴警报的准确性。该项目的主要科学目标是利用双极化雷达对液滴尺寸分布(DSD)矩的反演和一维(1D)模型的协同组合，更深入地了解支配液滴尺寸分布(DSD)演变的微物理过程--通过扫描极化C波段装甲雷达和阿拉巴马大学亨茨维尔分校(UAH)运营的移动综合轮廓系统(MIPS)对观测良好的案例进行矩和处理速率的预测。这些模拟是基于两个新的数值模式，(I)俄克拉荷马大学开发的云粒子模型(CPM)，它明确地解释了在暖雨微物理过程中所有云粒子的演化，以及(Ii)德国气象局开发的新的蒙特卡洛微物理模式(McSnow)，它基于“超级粒子”的物理性质模拟了冰、混合相和雨的演化。将使用三种不同的仪器在0.1-8毫米的整个尺寸范围内以良好的精度测量和表征DSD(气象粒子光谱仪、2D视频视频仪和降水发生传感器系统)。同时，仪器上方的体积将由双极装甲雷达以及MIPS扫描。这两个粒子模型将在一维空间中运行，并将DSD的演变与表面测量和雷达轮廓进行比较，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到地表之间的耦合。麦克斯诺预报的双极变星的廓线和斜率从亮带上方到地面的高度将与雷达观测进行比较，以推断主要过程和降雨类型。表面测量将作为模型预测的约束。几乎所有碰撞过程模拟中的一个中心假设是，雨滴以最终速度落下，仅取决于雨滴的质量。然而，最近对落速分布的一些观测表明，在湍流条件下，毫米级液滴可以显著偏离Gunn-Kinzer终端速度方程，这一点有待证实。显式云粒子模型将用于预测湍流引起的坠落速度偏差(均值和方差)是否对碰撞过程和随后的DSD演化产生重大影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.