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Chad W. Higgins, Eric Pardyjak, Martin Froidevaux, Valentin Simeonov, and Marc B. Parlange

; Katul and Parlange 1992 ; Parlange et al. 1993 ). In this study we measure the advection of water vapor near a lake–land transition using high-resolution Raman lidar measurements of the horizontal atmospheric water vapor distribution. We find that water vapor advection is not sufficient to account for the missing energy in the local energy balance. Next, an analytical description of the horizontal water vapor distribution based on the Sutton solution ( Brutsaert 1982 ; Sutton 1934 ) is proposed to

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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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Junchao Shi, Massimo Menenti, and Roderik Lindenbergh

the terrain characteristics and returned ICESat laser pulse. Next, we classify the RMS width observation data into groups based on the slope and roughness, respectively. Specifically, we group the RMS width data into three roughness groups: 0–0.7 m, 0.7–1.0 m, and >1.0 m (see Fig. 11 ). According to the range of the slope (0°–60°), the data in each group are further grouped in slope subclasses at 10° intervals (see Fig. 12 ), and nearly all the observations fall into the dashed outlined zone

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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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Rebecca Gugerli, Marco Gabella, Matthias Huss, and Nadine Salzmann

; Howat et al. 2018 ; Gugerli et al. 2019 ). Besides the often difficult or even impossible deployment on glaciers, these instruments suffer either from low accuracy or limited spatial coverage (e.g., Kinar and Pomeroy 2015 ; Nitu et al. 2018 ). Spatially continuous observations on glaciers at a high resolution can be obtained, for example, by helicopter-borne ground penetrating radar (e.g., Machguth et al. 2006 ; Sold et al. 2013 , 2016 ), terrestrial and airborne lidar scanning (e.g., Prokop

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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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Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

:// ), the same method as in the ARM continuous forcing dataset ( Xie et al. 2004 ; S. Tang et al. 2019 ). The ARM Best Estimate data products (ARMBE) ( Xie et al. 2010 ; ) provide the hourly cloud fraction profiles that are derived from the Active Remotely-Sensed Cloud (ARSCL; ) data, a combination of cloud radar, micropulse lidar, and ceilometer observations ( Clothiaux et al. 2000 ). The

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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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