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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

different locations, whereas ground-based observations are paramount for a detailed understanding of regional precipitation by capturing the temporal variability of precipitation at a given location. However, ground-based radars must contend with range limits, beam blockage, and surface precipitation representativeness issues when sampling far above the surface at long ranges ( Kidd et al. 2018 ). Spaceborne radars are less affected by mountain beam-blocking than ground-based systems ( Wen et al. 2013

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Xiang Ni, Chuntao Liu, and Edward Zipser

ground or airborne observations only infer the microphysical properties of deep convection from radar reflectivities at a local scale, but are not able to reveal the regional variations of microphysical properties from a global perspective. As the first spaceborne precipitation radar, the Precipitation Radar (PR; operating at Ku band, 13.8 GHz) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite has provided detailed perspectives of deep convection in various kinds of weather systems over

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

reflectivity ( Z ) to snowfall rate ( S ) tables are created and applied directly to both radar datasets in order to mitigate algorithm assumption effects. Equitable GPM DPR and CloudSat CPR global snowfall comparisons are enabled by considering these important issues. This study first compares native DPR, GMI, and CPR gridded global snowfall products for the April 2014–March 2017 time period. Large regional annual snowfall amount differences are produced by these disparate datasets. These results are

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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

1. Introduction Hail is one of the major weather hazards that impacts agriculture and transportation, as well as causing considerable damage to homes and vehicles. Surface hail occurrence and size observations are collected in limited regions and time scales with various approaches ( Zhang et al. 2008 ; Changnon et al. 2009 ; Punge and Kunz 2016 ). The lack of consistent, global surface observation networks for hail has limited the study of regional variations in severe hailstorm properties

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

1. Introduction At the global scale, precipitation estimation primarily relies on satellite-based observations ( Smith et al. 2007 ; Hou et al. 2014 ; Huffman et al. 2015 ; Grecu et al. 2016 ). However, over complex terrain regions, satellite precipitation estimates can be associated with significant error (particularly underestimation of heavy precipitation), due to variability and uncertainty introduced by orographic effects ( Roe 2005 ; Houze 2012 ; Mei et al. 2014 ; Derin et al. 2016

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Sarah D. Bang and Daniel J. Cecil

microwave–only climatology of Ni et al. (2017) , we see hail heavily represented in the tropics (latitudes lower than ~15°), whereas Cecil and Blankenship (2012) applied regional scaling to mitigate this effect. The radar-based climatology of Ni et al. (2017) depicted substantially less hail in the tropics than in the passive microwave climatology. In the tropics, the troposphere is deeper than at higher latitudes, and therefore tropical clouds have a deeper layer in which to grow, relative to

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

assimilation include the Goddard Earth Observing System (GEOS) ( Hou et al. 2000a , b , 2001 , 2004 ; Pu et al. 2002 ; Lin et al. 2007 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) operational system ( Lopez and Bauer 2007 ; Geer et al. 2008 ; Lopez 2011 , 2013 ), and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ( Lien et al. 2016 ; Shao et al. 2016 ). On a regional scale, studies have assimilated rain rates into models such as the

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

1. Introduction The impacts of future climate change on global and local scales are an important concern for the public, and how to estimate plausible future climate scenarios is a challenging task for researchers. For this purpose, the main source of information is global climate models (GCM). The GCM outputs are too coarse and cannot reflect the details on regional and local scales; therefore downscaling is needed to resolve this scale mismatch problem. Statistical downscaling (SD) is a

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

precipitation rate. The improvement is more salient for very low values of TB diff , corresponding to the most intense precipitation rates. Taking the parallax shift into account shall therefore efficiently improve the retrieval of the extreme convective precipitation systems over land. However, it is uncertain whether the relation between the observed 89V TB and the altitude of the center of gravity of the ice hydrometers derived regionally is valid globally over land surfaces. Future work will focus on

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

blizzard storm with the mesoscale MM5 model and a delta-Eddington-type radiative transfer (RT) model to produce a storm-scale database for snowfall retrieval using AMSU-B observations. Noh et al. (2009) used a large number of snowfall profiles from airborne, surface, and satellite radars, as well as an atmospheric RT model ( Liu 1998 ) to generate a regional database for snowfall retrievals using the AMSU-B data. The study used the NESDIS Microwave Land Surface Emissivity Model ( Weng et al. 2001

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