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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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