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Aaron R. Naeger, Brian A. Colle, Na Zhou, and Andrew Molthan

were found to have large biases given the fixed parameter assumptions applied to the predefined hydrometeor classes ( Molthan and Colle 2012 ). As a result, several double-moment schemes have been implemented into the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model over the past decade, such as the Thompson (THOM), Morrison (MORR), and the most recent predicted particle properties (P3) scheme. Detailed discussion of these BMPs is presented in section 2c . Precipitation and

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Robert Conrick, Joseph P. Zagrodnik, and Clifford F. Mass

Continental Convective Clouds Experiment (MC3E; Jensen et al. 2016 ) and the Iowa Flood Studies (IFloodS; Krajewski et al. 2013 ) have provided valuable surface-based observations of precipitation and facilitated radar retrievals of microphysical properties (e.g., Pippitt et al. 2015 ). In addition to NASA GPM GV, a number of other studies have developed radar retrievals for DSDs using similar suites of observations (e.g., Brandes et al. 2006 ; Cao et al. 2008 ; Zhang et al. 2006 ). However, these

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Ousmane O. Sy, Simone Tanelli, Stephen L. Durden, Andrew Heymsfield, Aaron Bansemer, Kwo-Sen Kuo, Noppasin Niamsuwan, Robert M. Beauchamp, V. Chandrasekar, Manuel Vega, and Michael P. Johnson

focus on conditions where the hydrometeors are prevalent in their solid state, and as “low-density” habits (i.e., not mixed phases from the melting layer of precipitation, hail, nor high-density graupel). With frozen particles, characterizing the MSP or PSD via radar reflectivity measurements is challenging for multiple reasons. The morphological diversity of frozen particles induces a large variability of their masses and electromagnetic properties. Mathematically, it is difficult to disentangle

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Robert Conrick and Clifford F. Mass

: Validating model clouds and their optical properties using geostationary satellite imagery . Mon. Wea. Rev. , 132 , 2006 – 2020 , https://doi.org/10.1175/1520-0493(2004)132<2006:VMCATO>2.0.CO;2 . 10.1175/1520-0493(2004)132<2006:VMCATO>2.0.CO;2 Thompson , G. , P. R. Field , R. M. Rasmussen , and W. D. Hall , 2008 : Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization . Mon. Wea. Rev. , 136 , 5095

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Andrew Heymsfield, Aaron Bansemer, Norman B. Wood, Guosheng Liu, Simone Tanelli, Ousmane O. Sy, Michael Poellot, and Chuntao Liu

(HVPS-3), collected cloud particle size distribution and cloud particle imagery data over the size range from about 50 μ m to >2 cm. The HVPS-3, which was mounted such that it looked up and down at particles passing through its sample volume, was used in this analysis. For a description of the use of the HVPS-3 probe in this orientation on the Citation aircraft, see Heymsfield et al. (2015) and Giangrande et al. (2016) . The PSDs were used to derive bulk cloud properties, including the ice water

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Joseph P. Zagrodnik, Lynn A. McMurdie, Robert A. Houze Jr., and Simone Tanelli

>−10 dB Z . The MRR was not functioning during the 13 Nov 2015 event. Two different types of disdrometers were used in this study. All three sites had second-generation Particle Size and Velocity 2 (PARSIVEL 2 ) disdrometers. The PARSIVEL 2 is a laser optical device that measures the size and fall velocity of hydrometeors passing through a 180 mm × 30 mm × 1 mm sheet laser. The raw output contains 32 size and velocity bins from 0.2- to 25-mm diameter with a time resolution of 10 s. The PARSIVEL 2

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

atmospheric column data file in netCDF format ( Unidata 2016 ). Distinguishing properties of each native dataset (including exact platform locations, operation modes, and algorithm versions) are maintained as attributes. Table 2 lists the sensor/data parameters preserved for each supported platform. This affords the simplicity of a single-file starting point for a wide variety of investigations. Extensive resources for reading netCDF files are widely available within the most prevalent coding tools used

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Annareli Morales, Hugh Morrison, and Derek J. Posselt

a small subset of the total number of parameters are responsible for most of the microphysics-induced variability in orographic precipitation. Although the results presented here focused on orographic precipitation, parameters such as WRA, ECI, and A s also showed effects on the liquid and ice water paths. While cloud radiative effects are beyond the scope of this study, we suggest that future work should explore the impact of microphysical parameter perturbations on cloud optical properties

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Yagmur Derin, Emmanouil Anagnostou, Marios Anagnostou, and John Kalogiros

information regarding the instrument characteristics and their properties can be found in Tables 1 and 2 . Four in situ stations were within the sampling area of DOW. It should be noted that version 2 DOW data have been created after discovering the discrepancy between DOW and NPOL. A new and more appropriate calibration method has been applied ( Houze et al. 2018 ), and in this study DOW version 2 has been used, which was downloaded from GHRC DAAC. Table 1. DOW and MRR properties. Table 2. In situ

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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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