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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 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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OPTIMIZATION-BASED SAMPLING IN ENSEMBLE KALMAN FILTERING

Volume 4, Numéro 4, 2014, pp. 349-364
DOI: 10.1615/Int.J.UncertaintyQuantification.2014007658
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RÉSUMÉ

In the ensemble Kalman filter (EnKF), uncertainty in the state of a dynamical model is represented as samples of the state vector. The samples are propagated forward using the evolution model, and the forecast (prior) mean and covariance matrix are estimated from the ensemble. Data assimilation is carried out by using these estimates in the Kalman filter formulas. The prior is given in the subspace spanned by the propagated ensemble, the size of which is typically much smaller than the dimension of the state space. The rank-deficiency of these covariance matrices is problematic, and, for instance, unrealistic correlations often appear between spatially distant points, and different localization or covariance tapering methods are needed to make the approach feasible in practice. In this paper, we present a novel way to implement ensemble Kalman filtering using optimization-based sampling, in which the forecast error covariance has full rank and the need for localization is diminished. The method is based on the randomize then optimize (RTO) technique, where a sample from a Gaussian distribution is computed by perturbing the data and the prior, and solving a quadratic optimization problem. We test our method in two benchmark problems: the 40-dimensional Lorenz '96 model and the 1600-dimensional two-layer quasi-geostrophic model. Results show that the performance of the method is significantly better than that of the standard EnKF, especially with small ensemble sizes when the rank-deficiency problems in EnKF are emphasized.

CITÉ PAR
  1. Solonen Antti, Cui Tiangang, Hakkarainen Janne, Marzouk Youssef, On dimension reduction in Gaussian filters, Inverse Problems, 32, 4, 2016. Crossref

  2. Full-Waveform Inversion II Complete Session, SEG Technical Program Expanded Abstracts 2016, 2016. Crossref

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