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Gift Dumedah and Jeffrey P. Walker

-00591. Dumedah, G. , and Coulibaly P. , 2013a : Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data . Adv. Water Resour. , 53 , 231 – 241 , doi:10.1016/j.advwatres.2012.07.012 . Dumedah, G. , and Coulibaly P. , 2013b : Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation . Adv. Water Resour. , 60 , 47 – 63 , doi:10.1016/j

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Rafael Pimentel, Javier Herrero, Yijian Zeng, Zhongbo Su, and María J. Polo

, obtained with data from 2004 to 2007. Two different performances of the model were tested. First, the three DCs were evaluated by comparing TP-observed values of SCF and snow depth with their simulated results, without any assimilation procedure (open-loop simulation). Second, the TP-SCF dataset (observation) was assimilated by means of ETKF for the same DC formulations, and the simulated snow depth values were validated against the TP–snow depth dataset. This section describes the different items in

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martijn J. Booij, Arjen Y. Hoekstra, and Jun Wen

these three newly developed kB −1 schemes for the Noah LSM has so far not been evaluated for different seasons across the Tibetan Plateau. Only Chen et al. (2010) have investigated modeling results obtained with the z 0h scheme by Y08 for 2-month premonsoon episodes. In this investigation, we evaluate the performance of those kB −1 schemes for a Tibetan Plateau site in different seasons. A long-term dataset collected at the Maqu station (33.88°N, 102.15°E at an altitude of about 3430 m

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Chiara Corbari and Marco Mancini

). Different statistical indexes are computed to evaluate the goodness of model estimates in terms of RET images and discharges. So the mean bias error (MBE), the absolute mean bias error (AMBE), the rms error (RMSE), the relative error (RE) and the absolute error (AE) are computed as follows: where X sim i is the i th simulated variable by FEST-EWB, X obs i is the i th measured variable, n the sample size, and the average observed variable. The simulated and observed variables are always

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Junchao Shi, Massimo Menenti, and Roderik Lindenbergh

83°S latitudes. The general quality is estimated as 20 m at 95% confidence for vertical accuracy and 30 m at 95% confidence for horizontal resolution. Because of the lack of GPS and other sources of ground control points, cloud contamination, and water-masking issues, the DEMs in high-elevation regions like the Nam Co lake and the Nyainqêntanglha range still need to be validated. 3. Methods a. Slope and roughness derived from ASTER GDEM To evaluate the GLAS performance over mountainous glacial

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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, and Valentijn R. N. Pauwels

retrievals and simulations are also found in forested areas and in the African desert, where errors in the retrievals are generally larger ( de Jeu et al. 2008 ). The low correlations between SMOS and GEOS-5 may also be due to a lower quality of model precipitation forcings over some of these areas. Fig . A1. Evaluation of GEOS-5 vs SMOS soil moisture: MD, ubRMSD, and R for both ascending and descending orbits during 1 Jan 2010 to 1 Oct 2012. Statistics are computed at each grid cell and then averaged

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Mustafa Gokmen, Zoltan Vekerdy, Maciek W. Lubczynski, Joris Timmermans, Okke Batelaan, and Wouter Verhoef

for the case of sparse vegetation (70 W m −2 reduction in RMSE) but also an overall improvement of the model performance (40 W m −2 reduction in RMSE). Figure 4 provides a flowchart of explaining acquisition of daily, monthly, and yearly ET by SEBS-SM. The SEBS-SM was run on a daily interval using MODIS input data with 1-km spatial resolution on thermal bands. The model output had some missing days due to either the cloud coverage or unreliable data masked out by the quality control of the

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