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

. The complementary use of assimilation techniques can reduce uncertainty in the model forecast, considering that field data for additional snow variables are available. There are many examples of snow simulation using different assimilation techniques, from simple methods such as direct insertion (DI; Liston et al. 1999 ; Malik et al. 2012 ) to more complex methods such as those derived from applications of Kalman filter ( Kalman 1960 ). In this group, a wide range of methodologies are found: the

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Gift Dumedah and Jeffrey P. Walker

members were combined with perturbed forcing data to determine the state ensemble in Eq. (3) : where is a vector of forecasted states at time t and is a vector of updated states for the previous time. The forcing data were perturbed by adding the noise γ t with covariance at each time step to generate its ensemble according to Eq. (4) : The JULES model was run forward in time to determine the ensemble predictions in Eq. (5) : where f 2 represents the JULES prediction. The observation

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Chiara Corbari and Marco Mancini

weighting technique. Moreover, the air temperature spatial distribution takes into account the reduction of temperature with altitude, with a constant lapse rate of −0.0065°C m −1 , while shortwave net radiation is distributed considering the effect of topography ( Corbari et al. 2011 ). The model solves the system between energy and mass balance at the ground surface: where SM (–) is the soil water content, P (mm) is the precipitation rate, R (mm) is the runoff flux, PE (mm) is the drainage flux

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

; Saleh et al. 2007 ); and Lit3 is similar to Lit2, but with Nr p = 0 and with the soil roughness h as used in SMOS Tb monitoring with CMEM ( Sabater et al. 2011 ) at the European Centre for Medium-Range Weather Forecasts (ECMWF). These three sets of literature values are used in two ways. First, we simulate Tb using the literature values for the microwave RTM parameters and compare the results against SMOS observations. Second, the literature values are used as prior constraints in the parameter

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

of many recent research efforts ( McCabe et al. 2008 ) because of their potential to provide spatially continuous and temporally recurrent estimates over regional to global scales ( Alsdorf and Lettenmaier 2003 ). Precipitation is regularly retrieved from multisensor microwave and infrared data using a variety of techniques (e.g., Joyce et al. 2004 ). One of the recent datasets is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), which is designed to

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