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The Impact of Raindrop Collisional Processes on the Polarimetric Radar Variables

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The Impact of Raindrop Collisional Processes on the Polarimetric Radar Variables 3052 JOURNAL OF THE ATMOSPHERIC SCIENCES VOLUME 71 The Impact of Raindrop Collisional Processes on the Polarimetric Radar Variables MATTHEW R. KUMJIAN* 1 Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado OLIVIER P. PRAT Cooperative Institute for Climate and Satellites, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina (Manuscript received 10 November 2013, in final form 7 March 2014) ABSTRACT The impact of the collisional warm-rain microphysical processes on the polarimetric radar variables is quan- tified using a coupled microphysics–electromagnetic scattering model. A one-dimensional bin-microphysical rain shaft model that resolves explicitly the evolution of the drop size distribution (DSD) under the influence of collisional coalescence and breakup, drop settling, and aerodynamic breakup is coupled with electromagnetic scattering calculations that simulate vertical profiles of the polarimetric radar variables: reflectivity factor at horizontal polarization ZH, differential reflectivity ZDR, and specific differential phase KDP. The polarimetric radar fingerprint of each individual microphysical process is quantified as a function of the shape of the initial DSD and for different values of nominal rainfall rate. Results indicate that individual microphysical processes (collisional processes, evaporation) display a distinctive signature and evolve within specific areas of ZH–ZDR and ZDR–KDP space. Furthermore, a comparison of the resulting simulated vertical profiles of the polarimetric variables with radar and disdrometer observations suggests that bin-microphysical parameterizations of drop breakup most frequently used are overly aggressive for the largest rainfall rates, resulting in very ‘‘tropical’’ DSDs heavily skewed toward smaller drops. 1. Introduction surface. Further, understanding how these processes af- fect radar measurements is critical for optimizing remote As raindrops descend toward the surface, the evolution quantitative precipitation estimates as well as studies of of their distribution of sizes is governed by a number of precipitation microphysics using remote sensing. microphysical processes. These processes include sedi- Another major impetus for understanding the evolu- mentation or size sorting, evaporation, aerodynamic and tion of raindrop size spectra is that storm dynamics can collisional breakup, and coalescence. Understanding the be strongly influenced by precipitation microphysics physics governing the evolution of the drop size distri- (Srivastava 1985, 1987; Markowski et al. 2002; Gilmore bution (DSD) is important because changes of the DSD et al. 2004; Dawson et al. 2010; Van Weverberg et al. 2011; affect the mass flux or rainfall rate experienced at the Bryan and Morrison 2012; Morrison et al. 2012). This is because processes like precipitation loading, evaporation of raindrops, and melting of hailstones generate negative * Current affiliation: Department of Meteorology, The Pennsyl- buoyancy that can affect storm behavior and evolution via vania State University, University Park, Pennsylvania. 1 The National Center for Atmospheric Research is sponsored downdraft and cold pool production. Thus, providing the by the National Science Foundation. best approximation to the physics governing precipitation processes is crucial for storm-scale modeling. Model simulations of convective storms have been found to be Corresponding author address: Dr. Matthew R. Kumjian, De- partment of Meteorology, The Pennsylvania State University, 513 quite sensitive to microphysical parameterizations, in- Walker Building, University Park, PA 16802. cluding warm-rain processes (Ferrier et al. 1995; Morrison E-mail: [email protected] et al. 2009; Dawson et al. 2010; Bryan and Morrison 2012). DOI: 10.1175/JAS-D-13-0357.1 Ó 2014 American Meteorological Society AUGUST 2014 K U M J I A N A N D P R A T 3053 As an example, Morrison et al. (2012) found large sensi- from near 0 dBZ or less in light drizzle to .50 dBZ in tivity to the drop breakup parameterization, including its extremely heavy downpours. The reflectivity-weighted substantial effects on storm characteristics such as cold shape of particles in the radar sampling volume is pool strength, propagation speed, and precipitation accu- measured by ZDR, where values of 0 dB indicate mulation. Though these parameterizations are based on spherical or randomly oriented hydrometeors and kernels and efficiencies developed from laboratory and increasing magnitudes of positive or negative values bin model studies, there still exists considerable uncer- indicate increasing particle anisotropy. In rain, values tainty (Morrison et al. 2012). vary from 0 dB in drizzle to 4–5 dB in heavy continental The processes that are the subject of the present study rain at S band (and up to 6 dB or more at C band). are the collisional processes of coalescence and breakup. Although ZDR is independent of particle concentration, There has been much experimental work in the labora- it does depend on the shape of the DSD. The tory with colliding drop pairs (McTaggart-Cowan and accumulated phase lag between the horizontally and List 1975; Low and List 1982a, hereafter LL82a; Ochs vertically polarized waves per unit radial distance is et al. 1995; Barros et al. 2008; among others) and, more given by KDP and thus measures the concentration of recently, direct numerical simulation of colliding drops anisotropic particles within the sampling volume. It is (Schlottke et al. 2010). These studies have been in- dependent on particle concentration, shape, size, and strumental in leading to the determination of collision composition but is not affected by spherical or randomly 21 efficiencies between drop pairs of various sizes and the oriented particles. Values of KDP vary from 08 km in 2 development of coalescence and breakup kernels used in drizzle to .48 km 1 (at S band) in very heavy rain. It is model parameterizations (Low and List 1982b, hereafter also inversely proportional to the radar wavelength, so LL82b; Feingold et al. 1988; Beard and Ochs 1995; shorter wavelengths like C and X band will produce McFarquhar 2004; Prat et al. 2012). Analytic and nu- proportionally larger KDP values for the same sampled merical models of the evolution of raindrop spectra un- rainfall. In rain, rhy is near unity, except for heavy dergoing these processes have also been widely studied continental rain at C band, in which values may drop (List and Gillespie 1976; Valdez and Young 1985; Brown as low as 0.93 owing to resonance scattering effects by 1987, 1988, 1993, 1997; List et al. 1987; Tzivion et al. 1989; large raindrops. List and McFarquhar 1990; Hu and Srivastava 1995; These polarimetric variables are affected by changes Seifert et al. 2005; Prat and Barros 2007a,b, 2009; Prat in the distribution of sizes and shapes of particles and et al. 2012), as have numerical methods for efficiently and thus can provide information about the physical pro- accurately solving the stochastic collection and breakup cesses acting on the precipitation. Such new information equation (Bleck 1970; Bott 1998, 2000, 2001; Prat and may be used to validate microphysical models of rain Barros 2007b; Jacobson 2011). physics as well as to ‘‘fingerprint’’ the dominant ongoing In contrast to the extensive modeling and laboratory processes in precipitation. Studies along these lines have studies mentioned above, there has been relatively little investigated the other warm-rain processes of evapora- work in validating these models and kernels in nature. tion (Kumjian and Ryzhkov 2010) and size sorting The emergence of dual-polarization radar offers added of raindrops (Kumjian and Ryzhkov 2012). Thus, one information regarding the evolution of the shape of the of the objectives of this study is to quantify the impact DSD. The variables available from polarimetric radar of coalescence and breakup of raindrops on the dual- measurements that will be discussed in this study are radar polarization radar variables, thereby determining the reflectivity factor at horizontal polarization ZH,differen- microphysical fingerprints of the processes in polari- tial reflectivity ZDR, specific differential phase KDP,and metric radar data. the copolar correlation coefficient rhy. A detailed de- To do this, we use the one-dimensional version of an scription of these polarimetric variables may be found in explicit bin-microphysical model (Prat and Barros various textbooks (e.g., Doviak and Zrnic 1993; Bringi 2007a,b; Prat et al. 2012) coupled with T-matrix electro- and Chandrasekar 2001) or review articles (e.g., Herzegh magnetic scattering calculations (e.g., Waterman 1969; and Jameson 1992; Zrnic and Ryzhkov 1999; Ryzhkov Mishchenko 2000) and the polarimetric radar oper- et al. 2005; Kumjian 2013a,b,c). Briefly, ZH is strongly ator of Ryzhkov et al. (2011). Though idealized and dependent on the equivalent volume diameter D of par- operating within a simplified (one dimensional) frame- ticles; it is proportional to D6 for particles that are elec- work, this type of modeling approach allows for efficient tromagnetically small compared to the radar wavelength. exploration of parameter space and isolation of partic- In addition, ZH depends on the concentration of particles ular processes. Results of the coupled microphysics– in the radar sampling volume. In rain, ZH generally in- scattering model are compared to disdrometer and creases for increasingly heavy rainfall rates and varies radar observations. 3054
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