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Guixing Chen, Xinyue Zhu, Weiming Sha, Toshiki Iwasaki, Hiromu Seko, Kazuo Saito, Hironori Iwai, and Shoken Ishii

small-scale complex surfaces is likely to improve our understanding of both land–atmosphere interaction and local weather variation ( Maronga and Raasch 2013 ). Because of the limited knowledge on roll convection and the difficulties in resolving complex surfaces, a substantial challenge is realistically forecasting sea-breeze HCRs over coastal cities ( Ashie and Kono 2011 ; Chen et al. 2015 , hereafter Part I ). Complex surfaces are defined by two major aspects: land use and geometry. Land-use

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Karen Kosiba, Joshua Wurman, Forrest J. Masters, and Paul Robinson

wind maps over Port Arthur using the space-filled DOW wind data. Corresponding to the DOW data coverage over Port Arthur, a 10 km × 10 km mapping domain was chosen and partitioned into 1600 squares of 0.25 km × 0.25 km in order to account for the effects of the inhomogeneous surface roughness on the 10-m radar-derived winds. Each of the 1600 squares was assigned a roughness length z 0 based on land use within that segment. Land use was determined through the use of Google Earth images, the

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Yu-Heng Tseng, Shou-Hung Chien, Jiming Jin, and Norman L. Miller

components are introduced briefly in the following subsections. Fig . 1. The structure of the I-RMS. a. Weather Research and Forecasting Model 3.1 with Community Land Model 3.0 (WRF-CLM) WRF-CLM is designed as a regional weather and climate model ( Skamarock et al. 2005 ; Skamarock and Klemp 2008 ) coupled with the Community Land Model ( Oleson et al. 2004 ), a land surface model used in the Community Climate System Model (CCSM). The CLM includes a sophisticated subgrid representation, advanced snow

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Augustin Colette, Nadja Leith, Vincent Daniel, Enrica Bellone, and David S. Nolan

to 50 hPa with about 9 levels below 800 hPa and 23 below 150 hPa. Turbulence in the planetary boundary layer follows the Yonsei University scheme. Convection is computed using the Kain–Fritsch formulation. The microphysics follows the WRF single-moment three-class scheme. Surface friction velocities and exchange coefficients are based on Monin–Obukhov theory with standard similarity functions from lookup tables. The land surface model computes the temperature of five layers but does not

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Y. Xue, F. J. Zeng, K. E. Mitchell, Z. Janjic, and E. Rogers

features in the sea–atmosphere interactions in different oceans are identified, a substantial effort needs to be made to identify the specifics of the mechanisms and influence of the land surface processes in different parts of the world. During the past decade, using coupled atmosphere–biosphere models, various studies have explored the effects of surface hydrology–atmosphere interactions over different continents. For example, research using the Center for Ocean–Land–Atmosphere Studies's general

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

close to unity, making atmospheric features difficult to identify against such a background because of the limited contrast. In addition, the land surface emissivity is variable in space and time and difficult to model. However, efforts have been made to estimate cloud liquid water over land, using a priori information on the surface properties ( Aires et al. 2001 ). From observations above ~80 GHz, cloud ice information has been extracted, from both imagers such as SSM/I and water vapor sounders

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Barry H. Lynn, David R. Stauffer, Peter J. Wetzel, Wei-Kuo Tao, Pinhas Alpert, Nataly Perlin, R. David Baker, Ricardo Muñoz, Aaron Boone, and Yiqin Jia

as TKE, and is tested here coupled to both the control (SLAB) and the newly introduced (PLACE) land surface scheme. The control land surface scheme uses a two layer force-restore method to compute the ground temperature as a function of the soil and vegetation characteristics defined by a lookup table. The surface physical characteristics include albedo, roughness length, emissivity, thermal inertia, and soil moisture availability. The ground temperature responds to surface radiation fluxes that

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Geng Xia, Matthew C. Cervarich, Somnath Baidya Roy, Liming Zhou, Justin R. Minder, Pedro A. Jimenez, and Jeffrey M. Freedman

. Figures 9a and 9b show the scatterplots of the LST changes between MODIS and 3Day runs, and between MODIS and CON runs from EXP1; Figs. 9c and 9d are the same but for EXP2. Evidently, R 2 for the LST changes is much smaller than that in Fig. 4 , indicating that the WRF Model has difficulties in reproducing the spatial variations of observed LST changes at pixel levels, possibly due to simplified representation of surface heterogeneities (e.g., elevations, land use/soil classifications). However

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Margaret A. LeMone, Mukul Tewari, Fei Chen, Joseph G. Alfieri, and Dev Niyogi

1. Introduction This paper is the third of a series that addresses the horizontal variability of sensible and latent heat fluxes, H and LE, and their representation in land surface models, this time in the form of the Noah land surface model–based High-Resolution Land Data Assimilation System (HRLDAS; Chen et al. 2007 ). In the first two papers ( LeMone et al. 2003 , 2007b ), we examined horizontal variability along the amply watered and densely vegetated eastern track in Fig. 1 , using

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Alan K. Betts, Fei Chen, Kenneth E. Mitchell, and Zaviša I. Janjić

of the explicit cloud water scheme. The intent of this paper is to provide some documentation of these two models’ performance at the land surface (which will be helpful for users of the GCIP data archives, as they were used for the ESOP-95 and the current ongoing GCIP archive, respectively), as well as provide guidance for the development of the next model cycle. We use the well-documented FIFE data from 1987 for running test cases, because this dataset has already proved of great value for

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