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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

, 2016 : Full access the microwave radiative properties of falling snow derived from nonspherical ice particle models. Part I: An extensive database of simulated pristine crystals and aggregate particles, and their scattering properties . J. Appl. Meteor. Climatol. , 55 , 691 – 708 , https://doi.org/10.1175/JAMC-D-15-0130.1 . 10.1175/JAMC-D-15-0130.1 Langille , R. C. , and R. S. Thain , 1951 : Some quantitative measurements of three-centimeter radar echoes from falling snow . Can. J. Phys

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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

( Førland et al. 2011 ). Additionally, the measurement of snow particles and the identification of the true amount of snow at the ground is a challenging task due to the complex and strongly variable microphysical properties of snow and ice crystals. For classical precipitation gauges, the liquid equivalent amount of snow is a direct measure; however, it is prone to large uncertainties especially in windy conditions ( Rasmussen et al. 2012 ) and still only a point information of precipitation. Radar

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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

scattering properties of ice particles that depend heavily on the kind of ice particles and the habit of snow crystals. In addition, in the CloudSat case, the effect of non-Rayleigh scattering and multiple scattering by ice particles makes the quantitative retrieval of precipitation rate very difficult in intense ice precipitation. The CloudSat ’s very narrow swath and sun-synchronous orbit limit the sampling representativeness as well. One of the objectives of the Global Precipitation Measurement

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Kenneth D. Leppert II and Daniel J. Cecil

height of 4 km. Fig . 1. (a) Gridded reflectivity and (b) the associated HID from KFWS valid 2225 UTC 26 May 2015 at a height of 4 km (about −2°C temperature). The hydrometeor types are drizzle (DZ), rain (RN), ice crystals (IC), aggregates (AG), wet snow (WS), vertically aligned ice (VI), low-density graupel (LG), high-density graupel (HG), hail (HL), and big drops (BD). The black cross indicates the location of the radar. Dolan and Rutledge (2009) and Dolan et al. (2013) developed a fuzzy

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Xiang Ni, Chuntao Liu, and Edward Zipser

conventional approach and ice particles are assumed as spherical. However, the shape of an ice particle is crucial for the simulation results. Kuo et al. (2016) discussed scattering properties of falling snow and found that the Ku-/Ka-band backscattering efficiency of nonspherical crystals departs from the lines of size-dependent densities results when liquid equivalent diameter is greater than 1–1.5 mm. The DFR between Ku and Ka bands is around 4 dB for 1.0-mm snow particles when the μ is set to 2

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

(dark green). Fig . 6. Attenuated backscatter shown by the 532-nm cloud physics lidar (CPL) aboard the ER-2 on 3 Dec 2015. Time is in UTC and height in km. Since the scattering cross section of cloud-top ice crystals is approximately twice their physical cross section, the CPL is very sensitive to the precise location of cloud top. The strength of the signal tends to penetrate approximately 3–3.5 optical depths, providing an approximate indication of the density of hydrometeors in the cloud

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Minda Le and V. Chandrasekar

ground radar hydrometeor type at the closest latitude and longitude data point. When the snow flag is 1 (surface snowfall exists), we consider hydrometeor types of dendrite (DN; in Bechini and Chandrasekar 2015 ), crystal (CR), and dry snow (DS) from ground radar to be a match. When the snow flag is 0 (surface snowfall does not exist), the other hydrometeor types from the ground radar identification algorithm are considered a match. The hydrometeor type identified from the ground radar is summarized

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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Kamil Mroz, Alessandro Battaglia, Timothy J. Lang, Simone Tanelli, and Gian Franco Sacco

of the trigger, a hierarchy of hydrometeor types is used to attribute a “dominant” hydrometeor class in the column. First, the maximal fraction of the DPR footprint of each hydrometeor type is calculated in the column, then the species that occupies more than 5% of the footprint with the highest priority is used as a dominant type. The hierarchy of hydrometeor classes in descending order is as follows: hail, high- and low-density graupel, aggregates, ice crystals, and, finally, a group that

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

-0039.1 Kuo , K. , and Coauthors , 2016 : The microwave radiative properties of falling snow derived from nonspherical ice particle models. Part I: An extensive database of simulated pristine crystals and aggregate particles, and their scattering properties . J. Appl. Meteor. Climatol. , 55 , 691 – 708 , https://doi.org/10.1175/JAMC-D-15-0130.1 . 10.1175/JAMC-D-15-0130.1 Le , M. , V. Chandrasekar , and S. Biswas , 2017 : An algorithm to identify surface snowfall from GPM DPR observations

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