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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

1. Introduction Snow plays an important role in the Earth energy exchange processes and is a fundamental element of the water cycle. Higher-latitude regions are experiencing significant modifications related to climate change. While the effect on temperatures is relatively well known, the impacts on precipitation, snow/ice extent, and snow/ice properties are less documented and less understood. The use of satellites for snowfall monitoring and quantification and for retrieving snow-cover

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

Johnson 2011 ). These characteristics are difficult to accurately parameterize as of today. Second, the already weak snowfall scattering signal tends to be masked by the increased atmospheric emissivity and liquid water content in precipitating conditions ( Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). Third, changes in surface emissivity due to snow accumulation on the ground can significantly alter the snowfall microwave signal. Dry snow cover scatters the upwelling surface

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

/ocean boundaries), respectively (see Grecu et al. 2004 , 2016 ). Alternatively, MRMS is used over snow-covered-land surfaces. One year of Ku-V04, CORRA-V04 retrievals (from September 2014 to August 2015) and two years of matched MRMS data (from April 2014 to August 2016) populate the a priori dataset. Out of the three different precipitation estimates provided by GPROF (surfacePrecipitation, mostLikelyPrecipitation, frozenPrecipitation), only the surfacePrecipitation product is assessed for the agreement

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

1. Introduction Solid precipitation and its deposition as snow are of great importance for Earth’s energy budget and its hydrological cycle. Especially in the Arctic and already at latitudes higher than 60°N, snowfall is the predominant precipitation type ( Levizzani et al. 2011 ). We know today that temperatures are rising about 2 times faster in the Arctic than anywhere else on Earth due to global warming ( IPCC 2007 ; Serreze and Barry 2011 ), known as Arctic amplification. This has a great

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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

Precipitation Measurement (GPM) Core Observatory , was launched in February 2014 and extends observations of both falling snow and heavy to light rain past 65°N/S ( Hou et al. 2014 ). To provide nearly global coverage with short revisit times, the TRMM and GPM missions rely on a constellation of partner satellites. The TRMM Multisatellite Precipitation Analysis (TMPA) covers the area from 50°N/S from 2000 to present ( Table 1 ), while the Integrated Multisatellite Retrievals for GPM (IMERG) covers 60°N

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George Duffy, Greg Mcfarquhar, Stephen W. Nesbitt, and Ralf Bennartz

< −30°C, (c),(g) −10° < T < −20C, and (d),(h) T > −10°C. T 4 and T 3 have D m values that cover about half the range of D m measured during T 2 and T 1 . A large difference in D m ranges between these regimes is expected, since PSDs in clouds with T > −20°C allow for the growth of dendrites that can aggregate to be the largest ice particles. Z Ku – D m and DWR– D m relationships from different temperature ranges tend to stay close to the composite empirical relationships, with

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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

. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et

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

, the reflectivity–snow rate ( Z–S ) relationships employed in radar-based algorithms, either explicitly or implicitly, depend on the particular assumptions about microphysical properties and their uncertainties that are made within the algorithms. For passive microwave retrievals as from GMI, snowfall estimates can also be affected by variable surface emissivity, especially over snow-covered surfaces. The land surface variable emissivity hinders falling snow detection compared to oceanic

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Minda Le and V. Chandrasekar

. The solid black line is the left boundary of the DPR outer swath. Black dashed lines are the boundaries of the DPR inner swath. In Fig. 4b , hydrometeor type from ground radar shows most of the scan is covered by crystal and dry snow, with a small area of scattered rain identified to the west of the radar. DPR reflectivity at 2-km height is in Fig. 4c and the corresponding snow flag is illustrated in Fig. 4d . The region between KCLE 100-km range (big circle) and the DPR inner swath is pretty

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Sarah D. Bang and Daniel J. Cecil

limitations, hail retrievals trained in one location (often the United States) may not translate to other locations around the globe. Cecil and Blankenship (2012) attempt to mitigate this issue by applying regional scaling factors to different regional boxes throughout the AMSR-E domain. Those scaling factors are based on empirical relationships between T b and radar profiles in each region. Surface snow or ice cover can be a major problem for retrievals at high latitudes, or over mountains. Icy or

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