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

atmospheric column (i.e., convergence). 4. Evaluation of model results The model simulation’s performance was evaluated using the following observation and analysis data: 1) 25-km and daily precipitation analysis data from the National Weather Service ( ); 2) stage-IV multisource (radar mixing with gauge) 4 km × 4 km, hourly precipitation data; 3) North American Regional Reanalysis (NARR) data ( ) with 3-hourly

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Xi Chen, Yongqin David Chen, and Zhicai Zhang

simulated and observed discharge for 1990–91 and 1994–95 examples are given in Fig. 4 , showing that the two time series match each other reasonably well. The Nash–Sutcliff index for the entire calibration period (1986–95) is 0.79, and RMSE is 4.0 m 3 s −1 . Model performance is also evaluated by comparing the simulated and observed groundwater table. As shown in Fig. 5 , the groundwater table is simulated quite accurately for individual observation wells such as 13, 29, and 35. Averages of the

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

) center and/or a hydrologic service center. GEWEX hydrometeorological studies frequently use satellite data to map water and energy cycle variables, to independently evaluate the performance and accuracy of models, and to assist in the validation of new sensors. At least 14 GEWEX projects are directed at hydrometeorological processes and modeling. Other GEWEX projects focus on the development and testing of new global products or on the development of the scientific underpinnings for new missions and

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Xia Zhang, Shu Fen Sun, and Yongkang Xue

, Minnesota, which were widely used for model evaluation ( Cherkauer and Lettenmaier 1999 ; Koren et al. 1999 ; Pauwels and Wood 1999 ; Warrach et al. 2001 ), and from the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME)/Tibet D66 site are used to test the model. Section 2 describes the frozen soil parameterization. Field data for evaluation are presented in section 3 . Simulation results and conclusions are given in sections 4 and 5 , respectively. 2. Model

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

Analysis ( Higgins et al. 2000 ), NCEP Regional Reanalysis ( Mesinger et al. 2006 ), and NCEP Land Data Assimilation System (NLDAS; Fan et al. 2006 ). To evaluate model performance against observations, one may compare the total values or the anomalies of individual fields. However, as in many previous studies (e.g., Fennessy and Shukla 2000 ; Palmer et al. 2004 ; Saha et al. 2006 ), model performance is often evaluated by examining anomaly fields because model climates are usually different from

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Guoxiong Wu, Yimin Liu, Qiong Zhang, Anmin Duan, Tongmei Wang, Rijin Wan, Xin Liu, Weiping Li, Zaizhi Wang, and Xiaoyun Liang

and Modeling of Atmospheric Variability: Selected Papers of Nanjing Institute of Meteorology Alumni in Commemoration of Professor Jijia Zhang , X. Zhu et al., Eds., World Scientific, 92–114 . Wu, G. X. , Wang J. , Liu X. , and Liu Y. M. , 2005 : Numerical modeling of the influence of Eurasian orography on the atmospheric circulation in different seasons. Acta Meteor. Sin. , 63 , 603 – 612 . Wu, T. W. , and Coauthors , 2003 : The performance of atmospheric component model R42L9 of

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