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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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AN ENSEMBLE KALMAN FILTER USING THE CONJUGATE GRADIENT SAMPLER

Volumen 3, Edición 4, 2013, pp. 357-370
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003889
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SINOPSIS

The ensemble Kalman filter (EnKF) is a technique for dynamic state estimation. EnKF approximates the standard extended Kalman filter (EKF) by creating an ensemble of model states whose mean and empirical covariance are then used within the EKF formulas. The technique has a number of advantages for large-scale, nonlinear problems. First, large-scale covariance matrices required within EKF are replaced by low-rank and low-storage approximations, making implementation of EnKF more efficient. Moreover, for a nonlinear state space model, implementation of EKF requires the associated tangent linear and adjoint codes, while implementation of EnKF does not. However, for EnKF to be effective, the choice of the ensemble members is extremely important. In this paper, we show how to use the conjugate gradient (CG) method, and the recently introduced CG sampler, to create the ensemble members at each filtering step. This requires the use of a variational formulation of EKF. The effectiveness of the method is demonstrated on both a large-scale linear, and a small-scale, nonlinear, chaotic problem. In our examples, the CG-EnKF performs better than the standard EnKF, especially when the ensemble size is small.

CITADO POR
  1. Bibov Alexander, Haario Heikki, Parallel implementation of data assimilation, International Journal for Numerical Methods in Fluids, 83, 7, 2017. Crossref

  2. Amour Idrissa, Kauranne Tuomo, A variational ensemble Kalman filtering method for data assimilation using 2D and 3D version of COHERENS model, International Journal for Numerical Methods in Fluids, 83, 6, 2017. Crossref

  3. Oliver Dean S., Metropolized Randomized Maximum Likelihood for Improved Sampling from Multimodal Distributions, SIAM/ASA Journal on Uncertainty Quantification, 5, 1, 2017. Crossref

  4. Amour I., Mussa Z., Bibov A., Kauranne T., Using ensemble data assimilation to forecast hydrological flumes, Nonlinear Processes in Geophysics, 20, 6, 2013. Crossref

  5. Parker Albert E., Pitts Betsey, Lorenz Lindsey, Stewart Philip S., Polynomial Accelerated Solutions to a Large Gaussian Model for Imaging Biofilms: In Theory and Finite Precision, Journal of the American Statistical Association, 113, 524, 2018. Crossref

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