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Alan K. Betts, Ahmed B. Tawfik, and Raymond L. Desjardins

, these opaque cloud observations can be calibrated to give the LW net and the SW cloud forcing, using the baseline surface radiation network ( BSRN 2016 ) station on the Prairies, 25 km south of Regina in Saskatchewan ( Betts et al. 2015 ). These opaque cloud observations are thus a surrogate for LW and SW cloud forcing (LWCF and SWCF), which determine much of the variability in the fully coupled land surface–cloud system on daily and longer time scales ( Betts et al. 2014a ; Betts and Tawfik 2016

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Liqing Peng, Zhongwang Wei, Zhenzhong Zeng, Peirong Lin, Eric F. Wood, and Justin Sheffield

with ensemble averaging techniques. Finally, we used a land surface model to investigate the sensitivity of surface energy and water balances to the uncertainty of radiation forcing. 2. Materials and methods a. Satellite datasets In this study, we used three different satellite products, including the International Satellite Cloud Climatology Project (ISCCP), the Surface Radiation Budget (SRB), and the University of Maryland Radiation Dataset (UMD). The details and inputs of these products are

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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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Jinwon Kim, Yu Gu, and K. N. Liou

1. Introduction The impact of aerosol radiative forcing on the energy and water cycle is an important concern in understanding regional climate, but the details of its spatiotemporal variability remain uncertain. Aerosols influence the energy and water cycle primarily via scattering and absorption of solar radiation (direct effect) and via their impact on the characteristics of clouds and precipitation (indirect effect). In climate modeling and long-range forecasts, uncertainties in aerosol

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Nicholas E. Wayand, Alan F. Hamlet, Mimi Hughes, Shara I. Feld, and Jessica D. Lundquist

grid size restrictions ( Ruiz-Arias et al. 2011 ) and representation of cloud cover ( Edwards and Slingo 1996 ). However, it is not clear whether empirical models that have been fitted to observed diurnal temperature range and relative humidity will perform as well with atmospheric model input that may be characteristically different from in situ observations. For each case of meteorological forcing data, we ask the following questions: How do simulated meteorological variables compare with in situ

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William Rudisill, Alejandro Flores, and James McNamara

from standard hourly WRF output variables. We calculate F WALL by differencing from the other terms in Eq. (1) . Consequently, there is a possibly significant residual value included in this calculation, since the WRF energy balance does not perfectly close with hourly output ( Porter et al. 2011 ). In an attempt to isolate the effect of the clouds on the overall energy budget response, we can define the cloud radiative forcing. We expect that there is some chaotic variability caused by any

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Prabhakar Shrestha

comparisons. Additionally, available in situ observations of surface energy fluxes, streamflow, and groundwater table depth (see online supplemental material) over the modeled domain are also used for model evaluation. 3. Results a. Precipitation and incoming solar radiation Clouds and precipitation are main source of uncertainties in weather prediction and climate simulations. While precipitation is an important forcing for integrated hydrological models, the modulation of incoming solar radiation by

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Raju Attada, Hari Prasad Dasari, Ravi Kumar Kunchala, Sabique Langodan, Kondapalli Niranjan Kumar, Omar Knio, and Ibrahim Hoteit

regional climate simulations by introducing subgrid-scale cloud-radiation interactions . J. Geophys. Res. Atmos. , 119 , 5317 – 5330 , https://doi.org/10.1002/2014JD021504 . 10.1002/2014JD021504 Hoell , A. , C. Funk , and M. Barlow , 2015 : The forcing of southwestern Asia teleconnections by low-frequency sea surface temperature variability during boreal winter . J. Climate , 28 , 1511 – 1526 , https://doi.org/10.1175/JCLI-D-14-00344.1 . 10.1175/JCLI-D-14-00344.1 Hong , S.-Y. , and J

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

://portal.nersc.gov/project/capt/ARMForcingData/tang32/VARANAL_subdomains/forcing_data/domainE/ ), 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 ; https://doi.org/10.5439/1095313 ) provide the hourly cloud fraction profiles that are derived from the Active Remotely-Sensed Cloud (ARSCL; https://doi.org/10.5439/1052058 ) data, a combination of cloud radar, micropulse lidar, and ceilometer observations ( Clothiaux et al. 2000 ). The

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Pierre Gentine, Albert A. M. Holtslag, Fabio D'Andrea, and Michael Ek

; Western et al. 2002 , 2004 ; Ronda et al. 2002 ; Skoien et al. 2003 ; Isham et al. 2005 ). The variations in land surface properties modify the water and energy flux partitioning at the land surface, which affect the state of the overlying atmosphere (turbulence, heat, moisture, stability, clouds, precipitation, and dynamics) ( Gentine et al. 2007 , 2010 , 2011b ; Findell et al. 2011 ; Gentine et al. 2011a ) and the near-surface turbulence, temperature, and moisture profiles ( Businger et al

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