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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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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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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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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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Mohammad Reza Ehsani, Ali Behrangi, Abishek Adhikari, Yang Song, George J. Huffman, Robert F. Adler, David T. Bolvin, and Eric J. Nelkin

distribution of AVHRR CP (i.e., shown with boxplots) for 2B-CLDCLASS-lidar clear sky and each cloud type, separately over land and ocean and for the Northern Hemisphere (NH) and Southern Hemisphere (SH) in high latitudes. This was obtained by identifying coincident observations between AVHRR and 2B-CLDCLASS-lidar with the temporal distance no more than 5 min, and spatial distance less than 5 km. The boxplots show the median values of AVHRR CP as well as the first and third quartiles (solid horizontal lines

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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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Viviana Maggioni, Humberto J. Vergara, Emmanouil N. Anagnostou, Jonathan J. Gourley, Yang Hong, and Dimitrios Stampoulis

1. Introduction Current runoff prediction systems integrate precipitation measurements into hydrological models that simulate river discharges at the watershed scale either distributed across the basin or as lumped values at the catchment outlet. As observations from rain gauges are nonexistent or sparse over several regions of the globe, remotely sensed rainfall measurements offer a unique and viable alternative source of forcing data for hydrological models (e.g., Su et al. 2008 ; Li et al

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