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Vahid Naeimi, Zoltan Bartalis, and Wolfgang Wagner

. It will take a few years into the MetOp mission before enough ASCAT data are gathered to update the scattering parameters and balance out any probable biases toward SCAT data. The first study region ( Fig. 3a ) in North America (region A) was chosen for its land cover diversity (crop-, grass-, and shrubland, and different forest types); its large water areas are excluded from the analysis. Also, a conservative snow climatology based on Special Sensor Microwave Imager (SSM/I) data ( Nolin et al

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Damian J. Barrett and Luigi J. Renzullo

satellite images or as spatially distributed datasets from a variety of sources. 1) Land cover classification We used the 1-km 2 land cover classification of the Australian Government Bureau of Rural Sciences that was prepared for the National Land and Water Resources audit (available online at http://adl.brs.gov.au/anrdl/php/ ). Land cover classes were grouped into four generalized categories: tall forests, shrublands, grasslands/crops, and water. Through the middle and northwest of the ROI is a mix

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Gabriëlle J. M. De Lannoy, Paul R. Houser, Niko E. C. Verhoest, and Valentijn R. N. Pauwels

computations to the observation space. The traditional innovation method ( Hollingsworth and Lönnberg 1986 ; Daley 1991 ; Houser et al. 1998 ) implicitly provides a way to find the a priori state (background) error correlations at observed locations and to use them to cover the larger part of state space by fitting a distance-dependent correlation function. This technique builds on the following second-order innovation statistics for each pair ( k , l ) of observation locations: where E

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M. F. P. Bierkens and L. P. H. van Beek

combinations of lead times and time of year at which the prediction is made. Finally, related to that is the question how the frequency and timing of initial hydrological state estimation impacts predictability. For instance, in areas with a large contribution of snowmelt to runoff a correct estimate of snow cover and snow depth at the end of the cold season would most likely be the prime factor explaining predictive skill ( Wood and Lettenmaier 2006 ). Acknowledgments We would like to acknowledge the help

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