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Haolu Shang, Li Jia, and Massimo Menenti

effects of raindrops on microwave observations at 37 GHz are not negligible. Geolocation and resampling introduce errors in the conversion from swath brightness temperature (BT) to the gridded data. Erroneous observations therefore need to be identified and removed. In this case, we applied the Harmonic Analysis of Time Series (HANTS) algorithm ( Menenti et al. 1993 ; Roerink et al. 2000 ; Verhoef 1996 ) to fill gaps and remove noisy samples. The fast Fourier transform (FFT) and HANTS have been

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Gift Dumedah and Jeffrey P. Walker

parameter errors can result in significant differences between the model prediction and observation. To account for several error sources in hydrologic models, Vrugt et al. (2005a , b ) have proposed a simultaneous optimization and data assimilation (SODA) procedure to merge the search capabilities of the Shuffled Complex Evolution Metropolis algorithm with the ensemble Kalman filter (EnKF), in order to estimate both model parameters and states. Moreover, Andreadis et al. (2008) have examined the

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Chiara Corbari and Marco Mancini

evapotranspiration fluxes (ET)]. In this way there is the opportunity to increase control points of evapotranspiration so that its accuracy can be improved. Satellite data for their intrinsic nature of spatially distributed information can be used for the internal calibration/validation of distributed hydrological models in each pixel of the domain. This can be achieved with hydrologic modeling based on energy and water balance algorithms in conjunction with remote sensing data, in particular of land surface

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

differences between the simulated and SMOS-observed Tb, without attempting to reduce the shorter-term errors that can be dealt with through Tb data assimilation. The RTM parameters are optimized locally, that is, for each grid cell independently, and for the land surface conditions simulated by the GEOS-5 modeling system. a. Objective function The particle swarm optimization (PSO; Kennedy and Eberhart 1995 ) search algorithm is used to maximize the Gaussian likelihood of a microwave RTM parameter set

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

–11). Every TP image underwent a two-step analysis: georeferencing, to provide the image with spatial coordinates, followed by a snow detection process using a nonsupervised clustering algorithm, which provided both the SCF and snow depth for each image. Both steps are described below. 1) Georeference The georeference of each image was made on the basis of the local DEM, whose quality together with the image quality (level of distortion induced by the lens during the acquisition process) determined the

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Junchao Shi, Massimo Menenti, and Roderik Lindenbergh

surfaces of varying relief, slope and roughness values for representative geomorphological terrains in our study area were quantified using ASTER GDEM. These two parameters were defined at a length scale of 90 m, approximating the footprint diameter of the GLAS laser beams by calculating the best-fitting local mean plane to a 3 × 3 grid of elevation values (see Fig. 2 ). The slope calculation [Eqs. (1) – (5) ] used the algorithm described by Burrough et al. 1998 : where dz / dx and dz / dy are

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

months between 1 April and 30 September). Fig . 2. Flowchart for determining the yearly rainfall, the snow water equivalent (SWE), and the total precipitation. All fluxes are in mm yr −1 . To estimate the rainfall distribution, we used the monthly product of the Tropical Rainfall Measuring Mission (TRMM-3B43) combined with local rain gauge measurements. The TRMM algorithm combines four independent sources: 1) the monthly average TRMM Microwave Imager (TMI) estimate, 2) the monthly average Special

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

to LE p , and the details can be found in Chen et al. (1996) . REFERENCES Bastiaanssen, W. G. M. , Menenti M. , Feddes R. A. , and Holtslag A. A. M. , 1998 : A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation . J. Hydrol. , 212–213 , 198 – 212 , doi:10.1016/S0022-1694(98)00253-4 . Brutsaert, W. H. , 1982 : Evaporation into the Atmosphere: Theory, History and Applications. Springer, 316 pp . Brutsaert, W. H. , 1998 : Land-surface water vapor and

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