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Nicholas Dawson, Patrick Broxton, and Xubin Zeng

stations across the CONUS. It has undergone extensive testing for consistency and robustness ( Broxton et al. 2016a , b ; see section 2 ) and has been used to evaluate a variety of operational weather and seasonal forecast models, reanalyses, and Land Data Assimilation Systems ( Broxton et al. 2016a , 2017 ; Dawson et al. 2016 ). In addition, as will be discussed later in section 3 , it performs well against independent airborne lidar data and moderate-resolution satellite snow cover data. In this

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Maheswor Shrestha, Lei Wang, Toshio Koike, Yongkang Xue, and Yukiko Hirabayashi

; Immerzeel et al. 2010 ). Seasonal snow cover is an important component of the Himalayan environment as precipitation occurs in solid form in the cold climate and regions of high elevation. Snow, with inherent properties such as high albedo, low roughness, and low thermal conductivity, has considerable spatial and temporal variability, which greatly governs the energy and water interactions between the atmosphere and land surface. From a hydrological point of view, the temporal and spatial variability of

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Jianhui Xu, Feifei Zhang, Hong Shu, and Kaiwen Zhong

-based direct insertion ( Fletcher et al. 2012 ; Liu et al. 2013 ; Rodell and Houser 2004 ; Zaitchik and Rodell 2009 ), the ensemble Kalman filter (EnKF; Andreadis and Lettenmaier 2006 ; Arsenault et al. 2013 ; De Lannoy et al. 2012 ; Su et al. 2008 ), the ensemble square-root filter (EnSRF; Clark et al. 2006 ; Slater and Clark 2006 ), the Bayesian scheme ( Kolberg et al. 2006 ), and the particle filter ( Thirel et al. 2011 , 2013 ) DA methods to assimilate the snow cover fraction (SCF; Arsenault

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Yves Lejeune, Ludovic Bouilloud, Pierre Etchevers, Patrick Wagnon, Pierre Chevallier, Jean-Emmanuel Sicart, Eric Martin, and Florence Habets

al. 2004 ; Sicart et al. 2005 ). However, although the impact of snow cover on the water supply is nonnegligible, the evolution and variability of snow cover on nonglacierized areas have not, to our knowledge, been studied on a local scale in this region. This present study focuses on the melting of the snow cover on a moraine site located at 4795 m MSL in the Charquini area (Bolivia, 16°17′S, 68°32′W) of the tropical Andes. Snowmelt is calculated by applying the energy balance to a control

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Sujay V. Kumar, Christa D. Peters-Lidard, Kristi R. Arsenault, Augusto Getirana, David Mocko, and Yuqiong Liu

spatially and temporally consistent estimates of snow conditions. Primarily, there are two types of spaceborne remotely sensed measurements of snow processes: 1) snow cover area (SCA) is typically measured using visible or infrared satellite sensors, exploiting the high reflectance of snow-covered areas compared to areas with no snow cover; and 2) passive microwave (PM)-based measurements of snow depth and snow water equivalent (SWE). Measurements made in the visible spectrum provide observations at

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Benjamin F. Zaitchik and Matthew Rodell

1. Introduction Average monthly snow cover in the Northern Hemisphere varies from 7% of all land area to more than 40%, making snow cover the fastest varying large-scale surface feature on Earth ( Chang et al. 1990 ; Hall 1988 ). This variability has a dramatic impact on surface moisture and energy fluxes. Snow insulates the ground beneath, moderating soil temperatures during winter ( Decker et al. 2003 ). Because of its high albedo, snow significantly reduces the absorption of radiation at

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Jiarui Dong, Mike Ek, Dorothy Hall, Christa Peters-Lidard, Brian Cosgrove, Jeff Miller, George Riggs, and Youlong Xia

contain large errors attributable to land surface complexities and temporally frequent snowmelt processes in the western United States (e.g., Tait and Armstrong 1996 ; Rodell et al. 2004 ; Foster et al. 2005 ; Dong et al. 2005 ; Tong et al. 2010 ), the 500-m daily Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) snow cover area (SCA) product has been widely used as an important constraint on snowpack processes in land surface and hydrological models. Assimilation

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Jicheng Liu, Curtis E. Woodcock, Rae A. Melloh, Robert E. Davis, Ceretha McKenzie, and Thomas H. Painter

1. Introduction Snow, because of its unique properties such as high albedo and low thermal conductivity, affects land surface radiation budgets and water balance ( Yang et al. 1999 ). Significant gains have been made in snow cover mapping using remotely sensed data in recent decades, but the presence of forests continues to present challenges ( Simpson et al. 1998 ; Hall et al. 1998 ; Hall et al. 2002 ; Dozier and Painter 2004 ). An understanding of the manner in which forest canopies

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Warren Helgason and John Pomeroy

and Gray 1997 ; Sauer et al. 1998 ; Pomeroy and Essery 1999 ; Hayashi et al. 2005 ; Pomeroy et al. 2006 ; Marks et al. 2008 ; Reba et al. 2009 ). Unfortunately, since the internal energy is rarely measured directly, there are no published measurements of the energy balance closure over snow-covered land; the degree of which can be a good indicator of the uncertainty in the measurements, and should be considered relative to the expectations of certain parameterizations that were derived from

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Anne W. Nolin and Christopher Daly

1. Introduction One of the most visible and widely felt impacts of climate warming is the change (mostly loss) of low-elevation snow cover in the midlatitudes. Temperature trends in the northwestern United States show a warming of 1°–2°C since the middle of the last century and related declines in snow cover ( Karl et al. 1993 ; Lettenmaier et al. 1994 ). Changes in snow cover are particularly pronounced in the Pacific Northwest region of the United States. Using measurements of 1 April snow

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