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

point, porosity, saturated hydraulic conductivity) on soil moisture and the mean water budget components under stochastic forcing. Here, potential means that the soil parameters are isolated from their original model, and their effect is evaluated using a parsimonious framework of stochastic soil moisture models. Through this methodology, we only evaluate the effect of parameters from different LSMs, not the LSMs themselves. Also, model-dependent compensating effects as a result of parameter

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Dongryeol Ryu, Wade T. Crow, Xiwu Zhan, and Thomas J. Jackson

sensed surface soil moisture retrievals (e.g., Margulis et al. 2002 ; Crow and Wood 2003 ; Reichle and Koster 2005 ; Reichle et al. 2007 ) into land surface models (LSMs) from local to global scales. In the EnKF approach, an ensemble of model states is generated by adding noise to state variables, model parameters, and/or forcing variables. The uncertainty of land surface model forecasts is then represented by the spread of these stochastically generated ensemble states. A Gaussian random number

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

.g., Gourley and Vieux 2005 ; Krzysztofowicz 2002 ). While calibrated NWP products are used to force hydrological models in traditional methods (e.g., Werner et al. 2005 ), one emerging approach is to use a coupled meteorological–hydrological modeling system (e.g., Verbunt et al. 2006 ). Since precipitation forecasts exhibit large uncertainties and many hydrological services consider the use of forecast precipitation introduces an unacceptable degree of uncertainty into their forecasts and makes the

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

sufficient accuracy, seasonal predictions may be attainable, even with average meteorological forcing. The main goal of this paper is to assess the skill of seasonal prediction of European discharge and how this skill varies between the branches of European rivers. Also, we aim to assess how the skill depends on use of NAO-based seasonal weather prediction, the properties of the river basin, and a correct assessment of initial hydrological states. The NAO-based seasonal weather prediction, which will be

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

optimal perturbation magnitude and structure for the forcings, model structure, state, and parameters. It is often useful to tune the filter by adapting the error variance—for example, by inflation, to avoid model divergence in case of an underestimated a priori error covariance. An adaptive inflation factor in time was proposed by Anderson (2007) , who adjusted the variance of each state component, whereas the correlation between pairs of components remained unchanged. Mitchell and Houtekamer (2000

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Damian J. Barrett and Luigi J. Renzullo

previous state, model parameters, and forcing). We also examine the effect of current and future errors in satellite observations on the resulting analysis error. 2. Observation operators and tangent linear models Two observation operators are developed in this work as well as their respective TLMs. In the context of this paper, H can be written as where the superscripts t and m distinguish the thermal and microwave observation operators, T s is land surface temperature, and T b is

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