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Niilo Siljamo and Otto Hyvärinen

1. Introduction With the growing number of satellite platforms and improvements in the processing and transmission of digital data obtained from them, it has become possible to obtain frequent snow-cover information in near–real time through a variety of different sources. Retrieving snow products from satellite data is still a challenging task. Topography, heterogeneity in snow distribution, the effects of slope, aspect, land use, wind, and other factors in the accumulation and melting periods

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Wenfang Xu, Lijuan Ma, Minna Ma, Haicheng Zhang, and Wenping Yuan

1. Introduction Snow cover plays an important role in regulating regional and global climate, especially in the Qinghai–Tibetan Plateau, because of its high surface albedo and heat-insulation effect, which influences the energy exchange between the land surface and atmosphere ( Barnett et al. 1988 ; Yang et al. 2001 ; Chapin et al. 2005 ; Euskirchen et al. 2007 ). More than a century ago, Blanford (1884) suggested that an inverse relationship existed between summer rainfall over

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Irene E. Teubner, Leopold Haimberger, and Michael Hantel

1. Introduction The relative snow cover duration n within a season is a parameter that can be used to describe the snow cover. It is defined here as the sum of days with a snow depth (SD) above a threshold s = 0 cm with respect to the total number of days in the considered period and, therefore, can take values from 0 to 1. Knowledge of the duration of the snow-covered period provides different applications. The variation of the snow cover duration can serve as a measure for climate change

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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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T. Nitta, K. Yoshimura, K. Takata, R. O’ishi, T. Sueyoshi, S. Kanae, T. Oki, A. Abe-Ouchi, and G. E. Liston

1. Introduction Seasonal snow cover is a key variable in the global climate system. For example, snow albedo feedback is important for climate change in heavily populated Northern Hemisphere extratropical landmasses, and its strength in the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3 and CMIP5) models exhibits a large spread ( Hall and Qu 2006 ; Qu and Hall 2014 ). Seasonal snow cover also plays an important role in the hydrological cycle. In Arctic rivers, changes in the

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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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Alexandre P. Fischer

1. Introduction a. Manual snow depth measurements In Canada, for all meteorological stations prior to the 1960s, and for most nonsynoptic stations over their entire history, snowboards were used to measure new snow within a specified time period ( Potter 1965 ). The depth of snow on the board was measured with a ruler that, since 1978, has been 1 m long and graduated every 0.2 cm. The board was then reset on the surface of the snow cover to prepare for the next snowfall (SF) event. A limitation

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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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Christopher G. Fletcher, Steven C. Hardiman, Paul J. Kushner, and Judah Cohen

1. Introduction Snow cover is a highly variable land surface condition that exerts a strong control on the heat and moisture budget of the overlying atmosphere ( Cohen and Rind 1991 ; Vavrus 2007 ). However, recent work has shown that regional snow cover variability can also lead to remote and even hemispheric-scale circulation responses. The most studied teleconnection is between variations in fall season Siberian snow cover and lagged changes in the winter northern annular mode (NAM

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Ross D. Brown and Philip W. Mote

1. Introduction Snow cover represents a spatially and temporally integrated response to snowfall events, and the sequence of snowfall and melt events determines not just the quantity of water stored as snow but also snowpack condition (e.g., grain size and compaction), which in turn determines avalanche risk, energy required for melting, albedo of snow, and much more. Snowpack takes on special significance in mountain regions where snow stores enormous quantities of water, altering the ecologic

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