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Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

forecast uncertainty, we must follow an ensemble approach. While various techniques, such as an ensemble preprocessor, are used to regenerate ensemble members, direct use of the NWP ensemble members ( Pappenberger et al. 2005 , 2008 ) provides another alternative approach. It has the advantage of facilitating the coupled meteorological–hydrological modeling. Traditionally, hydrological forecast is made for individual river basins, and the predicted streamflow is valid at the outlet of the river basin

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

moisture profile is available every 20 m (assume it representative for a 10 × 10 m 2 area) and the model simulates at a 10-m resolution, then the correlation between the forecast errors of two neighboring grid cells at 10 m apart will not be retrieved by the proposed technique, whereas the correlation between forecast errors in two observed grid cells at 20 m apart can be found. One limitation of the proposed method is the need to determine the adaptive information based on as many observations as

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

predictions lead to reasonable skill are those regions where winter precipitation is highly correlated with NAO phase, such as Scandinavia, the Iberian Peninsula, and the regions around the Black Sea. From our study one may arrive at the conclusion that, given the limited skill at which we are currently able to predict the NAO index of the coming season, seasonal forecasting of river discharge requires the development of accurate nowcasting systems using good models, new observational techniques (e

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Adriaan J. Teuling, Remko Uijlenhoet, Bart van den Hurk, and Sonia I. Seneviratne

; Galantowicz et al. 1999 ; Reichle and Koster 2005 ), latent heat fluxes (e.g., van den Hurk et al. 1997 ; Schuurmans et al. 2003 ), or to a combination of these (e.g., Seuffert et al. 2003 ). While data assimilation provides a pragmatic solution to momentarily improve soil moisture states, later biases are not prevented since most data assimilation approaches deal with model states rather than parameters (in contrast to calibration). In fact, many data assimilation techniques assume bias-free models

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Damian J. Barrett and Luigi J. Renzullo

1. Introduction Increasingly, methods of data assimilation are being applied to both hydrological and hydrometeorological problems driven by prospects of better characterization of initial conditions and improved forecasting skill ( Mecikalski et al. 1999 ; Reichle et al. 2001 ; Crosson et al. 2002 ; Reichle et al. 2002 ; Heathman et al. 2003 ; Merlin et al. 2006 ; Pan et al. 2008 ; Wang and Cai 2008 ; Barrett et al. 2008 ). The benefits afforded by the application of data assimilation

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