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000016635 0247_ $$2DOI$$a10.1016/j.advwatres.2011.04.014
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000016635 084__ $$2WoS$$aWater Resources
000016635 1001_ $$0P:(DE-HGF)0$$aZhou, H.$$b0
000016635 245__ $$aAn approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering
000016635 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2011
000016635 300__ $$a844 - 864
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000016635 440_0 $$020263$$aAdvances in Water Resources$$v34$$x0309-1708$$y7
000016635 500__ $$3POF3_Assignment on 2016-02-29
000016635 500__ $$aThe authors gratefully acknowledge the financial support by ENRESA (project 0079000029). The financial aid from the China Scholarship Council (CSC) to the first author is appreciated and extra travel grants from the Ministry of Education (Spain) awarded to the first and fourth authors are also acknowledged.
000016635 520__ $$aThe ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non-linearities: however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions). (C) 2011 Elsevier Ltd. All rights reserved.
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000016635 65320 $$2Author$$aNon-Gaussian
000016635 65320 $$2Author$$aEnsemble Kalman filter
000016635 65320 $$2Author$$aParameter identification
000016635 65320 $$2Author$$aData assimilation
000016635 65320 $$2Author$$aUncertainty
000016635 65320 $$2Author$$aGroundwater modeling
000016635 650_7 $$2WoSType$$aJ
000016635 7001_ $$0P:(DE-HGF)0$$aGomez-Hernandez, J.$$b1
000016635 7001_ $$0P:(DE-Juel1)VDB99007$$aHendricks Franssen, H.J.$$b2$$uFZJ
000016635 7001_ $$0P:(DE-HGF)0$$aLi, L.$$b3
000016635 773__ $$0PERI:(DE-600)2023320-6$$a10.1016/j.advwatres.2011.04.014$$gVol. 34, p. 844 - 864$$p844 - 864$$q34<844 - 864$$tAdvances in water resources$$v34$$x0309-1708$$y2011
000016635 8567_ $$uhttp://dx.doi.org/10.1016/j.advwatres.2011.04.014
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