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Binayak P. Mohanty and Jianting Zhu

intercomparison project show that soil moisture anomalies consistently produce precipitation anomalies in certain “hot spot” regions around the globe. The ability to predict soil moisture anomalies and related land surface fluxes and states requires a comprehensive approach combining the latest scientific understanding, modeling capabilities, and available remotely sensed observations. Land surface models require three types of inputs: initial conditions, atmospheric–soil boundary conditions–forcings, and

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Richard G. Lawford, John Roads, Dennis P. Lettenmaier, and Phillip Arkin

effects on precipitation predictability, Koster et al. (2004) postulated that areas with average precipitation frequency tend to have different characteristics in terms of their contributions to the predictability of seasonal climate than areas with very frequent or infrequent precipitation. a. Observational issues Errors and uncertainties in numerical prediction models result from poorly specified initial conditions, inaccurate boundary conditions, inadequate parameterizations, and inefficient

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Yefim L. Kogan, Zena N. Kogan, and David B. Mechem

and Doppler velocity, V d , and (c) radar reflectivity and Doppler velocity spectrum width, σ d . As radar reflectivity represents the sixth moment of the drop size distribution (DSD), one can expect it to be correlated with other moments of the DSD, such as liquid water content Q l (third moment of DSD), or drizzle flux R , which in stratocumulus clouds is proportional to the fourth DSD moment. Thus, a number of studies have been devoted to retrievals of Q l and R in boundary layer

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Ana M. B. Nunes and John O. Roads

profiles produced by the assimilation scheme, we also compare the surface radiation terms of all simulations to the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) datasets. In section 2 we describe the precipitation assimilation procedure, the regional model principal features, the initial and boundary conditions, and datasets used by the assimilation procedure, and we evaluate all simulations. In section 3 , the experiment results as well as some of the

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Song Yang, S-H. Yoo, R. Yang, K. E. Mitchell, H. van den Dool, and R. W. Higgins

surface boundary forcing? In this study, we investigate the response of model-simulated atmosphere, especially the response of seasonal precipitation and temperature, to the high-frequency variability of soil moisture using the National Centers for Environmental Prediction (NCEP) Eta Regional Climate Model (Eta Model). We investigate the summers of 1988 and 1993 when very different hydroclimate conditions emerged. The contiguous United States experienced the warmest and driest climate conditions in

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Xia Zhang, Shu Fen Sun, and Yongkang Xue

convergence is reached even when drastic phase change occurs. b. Boundary conditions The upper boundary condition for Eq. (1) on a bare soil surface is given by the vertical moisture flux q s θ (m s −1 ) and is defined as where E is evaporation rate (m s −1 ), U p is rainfall rate (m s −1 ) on the soil surface, and R s is surface runoff. If there is snow falling, the snow will create a snow cover over bare soil and a snow cover model, such as the snow–atmosphere–soil transfer model (SAST

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Xi Chen, Yongqin David Chen, and Zhicai Zhang

of treating soil moisture below the root zone as groundwater, which is used as a “boundary condition” in the simulation of soil moisture dynamics. In river hydraulics and hydrodynamics of open channels, a porous subsurface is seldom considered to be an active participant of in-channel processes and dynamics. In atmospheric science, soil moisture and groundwater are represented as “buckets” of limited sizes in which water movement is not coupled with streamflow dynamics. For other scientists and

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Jinwon Kim and Hyun-Suk Kang

-ridge moisture transport over the foothills and western slope of the Sierra Nevada could have contributed to heavy rainfall during an extreme precipitation event in February 1986 over the Feather River basin in the northern Sierra Nevada. Details of the impact of U p induced by the Sierra Nevada on precipitation according to large-scale inflow conditions, however, remain to be understood. The two components of terrain-induced wind disturbances, U p and U c , are related, and their occurrences and

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J. Li, X. Gao, and S. Sorooshian

States and northern Mexico at a 36-km grid resolution. Domain 3 (D3) covers the southwestern United States, northern Mexico, and southern Utah and Colorado at a 12-km grid resolution. Finally, domain 4 (D4), at a 4-km grid resolution, covers the upper Rio Grande basin. Figure 2 shows the topography (kilometer), dominant vegetation types, and soil types in domain 4. The solid-line region in Fig. 2 represents the boundary of the upper Rio Grande basin that was used in this paper. Figure 2

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Xubin Zeng and Aihui Wang

. 3c,d ). With the use of (1) – (3) , January R n and G change by less than 3 W m −2 , but the partitioning between LH (5.4 W m −2 ) and SH (−0.3 W m −2 ) is significantly changed. Associated with this change in partitioning, January ground temperature is also changed by 0.6 K ( Fig. 3b ). Figure 3 also shows that, in months with L t ≥ 2 (May–October), results from both simulations are very similar, and the small differences are caused by different soil moisture conditions in the earlier

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