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An Approach to Handling Non-Gaussianity of Parameters and State Variables in Ensemble Kalman Filtering

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An Approach to Handling Non-Gaussianity of Parameters and State Variables in Ensemble Kalman Filtering

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dc.contributor.author Zhou ., Haiyan es_ES
dc.contributor.author Gómez-Hernández, J. Jaime es_ES
dc.contributor.author Hendricks Franssen, Hendrikus Johannes W es_ES
dc.contributor.author Li, Liangping es_ES
dc.date.accessioned 2013-07-03T11:45:15Z
dc.date.issued 2011
dc.identifier.issn 0309-1708
dc.identifier.uri http://hdl.handle.net/10251/30483
dc.description.abstract [EN] The 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). © 2011 Elsevier Ltd. es_ES
dc.description.sponsorship The 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. en_EN
dc.language Inglés es_ES
dc.publisher ELSEVIER SCI LTD es_ES
dc.relation.ispartof ADVANCES IN WATER RESOURCES es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Data assimilation es_ES
dc.subject Ensemble Kalman filter es_ES
dc.subject Groundwater modeling es_ES
dc.subject Non-Gaussian es_ES
dc.subject Parameter identification es_ES
dc.subject Uncertainty es_ES
dc.subject Aquifers es_ES
dc.subject Data processing es_ES
dc.subject Gaussian distribution es_ES
dc.subject Gaussian noise (electronic) es_ES
dc.subject Groundwater flow es_ES
dc.subject Groundwater resources es_ES
dc.subject Kalman filters es_ES
dc.subject Real variables es_ES
dc.subject Standards es_ES
dc.subject Vectors es_ES
dc.subject Algorithm es_ES
dc.subject Aquifer es_ES
dc.subject Fluvial deposit es_ES
dc.subject Gaussian method es_ES
dc.subject Hydrogeology es_ES
dc.subject Kalman filter es_ES
dc.subject Numerical model es_ES
dc.subject Real time es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title An Approach to Handling Non-Gaussianity of Parameters and State Variables in Ensemble Kalman Filtering es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1016/j.advwatres.2011.04.014
dc.relation.projectID info:eu-repo/grantAgreement/ENRESA//0079000029/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Zhou ., H.; Gómez-Hernández, JJ.; Hendricks Franssen, HJW.; Li, L. (2011). An Approach to Handling Non-Gaussianity of Parameters and State Variables in Ensemble Kalman Filtering. ADVANCES IN WATER RESOURCES. 34(7):844-864. https://doi.org/10.1016/j.advwatres.2011.04.014 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.advwatres.2011.04.014 es_ES
dc.description.upvformatpinicio 844 es_ES
dc.description.upvformatpfin 864 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 34 es_ES
dc.description.issue 7 es_ES
dc.relation.senia 198266
dc.contributor.funder Empresa Nacional de Residuos Radiactivos es_ES
dc.contributor.funder China Scholarship Council es_ES
dc.contributor.funder Ministerio de Educación


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