Collaborative Research: Observational and Numerical Modeling Studies of Rain Microphysics

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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.