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

1. Introduction Despite being confined to high latitudes and altitudes when occurring at the surface, snow can be related to approximately 50% by number ( Field and Heymsfield 2015 ) and approximately 60% by mass accumulation ( Heymsfield et al. 2020 ) of all precipitation across cold and warm climates. Thus, the accurate retrieval of snow properties is required for an accurate quantification of the hydrologic cycle. Furthermore, the quantitative retrieval of snowfall properties is invaluable

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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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Xiang Ni, Chuntao Liu, and Edward Zipser

limited area model intercomparison simulations using TWP-ICE observations: 2. Precipitation microphysics . J. Geophys. Res. Atmos. , 119 , 13 919 – 13 945 , https://doi.org/10.1002/2013jd021372 . 10.1002/2013JD021372 Wang , Z. , G. M. Heymsfield , L. Li , and A. J. Heymsfield , 2005 : Retrieving optically thick ice cloud microphysical properties by using airborne dual-wavelength radar measurements . J. Geophys. Res. , 110 , D19201 , https://doi.org/10.1029/2005JD005969 . 10

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

F. Weng , 2010 : Uncertainties in microwave optical properties of frozen precipitation: Implications for remote sensing and data assimilation . J. Atmos. Sci. , 67 , 3471 – 3487 , https://doi.org/10.1175/2010JAS3520.1 . 10.1175/2010JAS3520.1 Kulie , M. S. , M. J. Hiley , R. Bennartz , S. Kneifel , and S. Tanelli , 2014 : Triple frequency radar reflectivity signatures of snow: Observations and comparisons to theoretical ice particle scattering models . J. Appl. Meteor

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Ali Tokay, Leo Pio D’Adderio, Federico Porcù, David B. Wolff, and Walter A. Petersen

parameters at the site. Early disdrometer-based spatial variability studies employed only three ( Tokay and Bashor 2010 ) or four ( Lee et al. 2009 ; Schröer 2011 ) units, and their findings were limited to quantifying the spatial variability within a single satellite footprint. With the availability of cost effective laser-optical disdrometers (Parsivel and Thies) there has been a noticeable increase in the number of disdrometer-based spatial variability studies. Tapiador et al. (2010) demonstrated

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Paloma Borque, Kirstin J. Harnos, Stephen W. Nesbitt, and Greg M. McFarquhar

presence of supercooled liquid water ( Brown 1982 ). Baumgardner et al. (2017) and references therein describe the different in situ probes and measurements that are used to characterize the microphysical properties of ice clouds. Two-dimensional cloud probe data from the UND Citation were processed by the System for Optical Array Probe Data Analysis (SODA) software developed by the National Center for Atmospheric Research. PSDs were obtained from a combination of observations from the Cloud Imaging

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Kenneth D. Leppert II and Daniel J. Cecil

inhomogeneity on the microwave optical properties of graupel and hailstones . IEEE Trans. Geosci. Remote Sens. , 55 , 6366 – 6378 , https://doi.org/10.1109/TGRS.2017.2726994 . 10.1109/TGRS.2017.2726994 Vivekanandan , J. , J. Turk , and V. N. Bringi , 1991 : Ice water path estimation and characterization using passive microwave radiometry . J. Appl. Meteor. , 30 , 1407 – 1421 , https://doi.org/10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2 . 10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

lead to larger object areas and more contiguous properties, while higher thresholds result in isolating the central convective parts of storms ( Bytheway and Kummerow 2015 ). The selected threshold in this study is 1 mm h −1 , applied on the datasets convolved by a 3 pixel × 3 pixel window. To exclude the issues in calculating the storm object properties, a threshold of 10 pixels (1000 km 2 ) has been considered in tracking the storm objects. In addition to MTD, further analysis was performed to

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

snowfall at an effective resolution of 5 km. Using the GMI, ○ quantify rain rates between 0.2 and 60.0 mm h −1 and ○ detect snowfall at an effective resolution of 15 km. Estimate the precipitation particle size distribution (e.g., quantitative estimates of precipitation microphysical properties such as the mean median mass diameter of the particle size distribution to within ±0.5 mm). Provide calibrated ground-based precipitation measurements and associated error characterizations at 50-km horizontal

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

Applications, version 2 (MERRA2; Gelaro et al. 2017 ). The NASA Land Information System (LIS) ( Peters-Lidard et al. 2007 ) provides observation-corrected land surface conditions. Because remote-sensed passive microwave measurements respond directly to scattering optical properties of precipitating particles in the atmosphere, rather than to surface precipitation amounts, the microphysics in the model is a crucial component for the application of radiance-based precipitation and cloud data assimilation

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