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Qian Cao, Thomas H. Painter, William Ryan Currier, Jessica D. Lundquist, and Dennis P. Lettenmaier

comparisons with radar rainfall estimates (e.g., Stampoulis et al. 2013 ; Gebregiorgis et al. 2017 ), gauge observations (e.g., Mei et al. 2014 ; Prat and Nelson 2015 ; Miao et al. 2015 ), and merged radar and gauge rainfall estimates such as the National Centers for Environmental Prediction (NCEP) Stage IV ( Lin and Mitchell 2005 ) products (e.g., Gourley et al. 2010 ; Mehran and AghaKouchak 2014 ). Radar precipitation estimates are subject to errors from, for example, radar calibration, beam

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William Ryan Currier, Theodore Thorson, and Jessica D. Lundquist

, snow course observations, and lidar, described in section 3 ). We used these observations to evaluate the ability of PRISM and a high-resolution (4/3 km) atmospheric model simulation (WRF; Mass et al. 2003 ) to determine frozen precipitation throughout water year (WY) 2016 and during individual storm events (focused on the OLYMPEX intensive observational period from November to December 2015). This paper is organized as follows. Section 2 provides background information on previous evaluations

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Hatim M. E. Geli, Christopher M. U. Neale, Doyle Watts, John Osterberg, Henk A. R. De Bruin, Wim Kohsiek, Robert T. Pack, and Lawrence E. Hipps

covered with mixed natural vegetation with variable height interspersed with bare soils, z 0 and d need to be estimated reasonably well from h c and this could be an important issue. The recent and significant advances in the remote sensing technique known as light detection and ranging (lidar) has resulted in the unprecedented capability of providing highly accurate representation of the earth’s surface and its features. The lidar in this study is a system consisting of a sensor that emits a

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Graham A. Sexstone, Colin A. Penn, Glen E. Liston, Kelly E. Gleason, C. David Moeser, and David W. Clow

their interactions with topography and land surface features such as forests (e.g., Elder et al. 1991 ) over a range of spatial scales (e.g., Blöschl 1999 ; Deems et al. 2006 ; Lopez-Moreno et al. 2015 ; Sexstone and Fassnacht 2014 ). For example, in alpine areas, wind redistribution of snow can create large snowdrifts that are not represented by station observations that are typically located below tree line. Furthermore, widespread changes in forest health, structure, and density associated

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

regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud

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Nicola Montaldo, Matteo Curreli, Roberto Corona, Andrea Saba, and John D. Albertson

the Sivapalan et al. (1987) approach as (4) Σ = { ⁡ [ 2 k s ⁡ ( θ s − θ ) 2 ⁡ ( − ψ b θ s − θ r ) ] ⁡ [ 1 ⁡ ( 2 b + 3 ) + 1 / 2 b + θ s − θ r θ s − θ ] } 1 / 2 , where ψ b is the air entry suction head. 2) Estimating the k s parameter for the infiltration model Using the observations of discharge and soil moisture from each experiment, the parameters of the infiltration model were calibrated to minimize the errors between the modeled and observed discharges. Indeed, based on the difference

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Ryan Gonzalez and Christian D. Kummerow

relationships of snow depth and physical predictor variables ( Balk and Elder 2000 ; Molotch et al. 2005 ; López-Moreno and Nogués-Bravo 2006 ). Fassnacht et al. (2003) and Dawson et al. (2016) used linear regression and piecewise linear regression, respectively, to distribute Snowpack Telemetry (SNOTEL) snow depth measurements across different elevation ranges. These methods have shown success in producing gridded estimates of snow depth that are consistent with observations; however, they are not

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Xuejian Cao, Guangheng Ni, Youcun Qi, and Bo Liu

://doi.org/10.1016/j.jclepro.2019.03.028 . 10.1016/j.jclepro.2019.03.028 Lyu , H. , G. Ni , X. Cao , Y. Ma , and F. Tian , 2018 : Effect of temporal resolution of rainfall on simulation of urban flood processes . Water , 10 , 880 , https://doi.org/10.3390/w10070880 . 10.3390/w10070880 Ozdemir , H. , C. C. Sampson , G. A. M. de Almeida , and P. D. Bates , 2013 : Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data . Hydrol. Earth Syst

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Don Cline, Simon Yueh, Bruce Chapman, Boba Stankov, Al Gasiewski, Dallas Masters, Kelly Elder, Richard Kelly, Thomas H. Painter, Steve Miller, Steve Katzberg, and Larry Mahrt

approximately 1280 m above ground level (AGL) via airborne lidar, normalized to ground controls and processed to remove noise and redundancies ( Corbley 2003 ). The elevation observations have approximately 1.5-m horizontal spacing and approximately 0.05-m vertical tolerances. The pixel size of the orthophotographs is 0.15 m. The snow-free and snow-covered elevation data with the orthoimagery provide detailed information about the distribution of snow depth in relation to vegetation distribution and height

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Yan Zhang, James A. Smith, Alexandros A. Ntelekos, Mary Lynn Baeck, Witold F. Krajewski, and Fred Moshary

the frontal zone are based on high-resolution rainfall fields derived using the Hydro-Next Generation Weather Radar (NEXRAD) system ( Krajewski et al. 2007 ). Volume scan radar reflectivity observations and cloud-to-ground (CG) lightning observations from the National Lightning Detection Network (NLDN) are used to examine convective evolution of organized thunderstorm systems embedded in the frontal zone. Disdrometer and lidar observations are used to examine microphysical processes associated

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