Relevant bibliographies by topics / Ensemble Kalman filter hyper-localized

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Academic literature on the topic 'Ensemble Kalman filter hyper-localized'

Author: Grafiati
Published: 14 December 2024
Last updated: 31 July 2025

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Journal articles on the topic "Ensemble Kalman filter hyper-localized"

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Nerger, Lars. "On Serial Observation Processing in Localized Ensemble Kalman Filters." Monthly Weather Review 143, no. 5 (2015): 1554–67. http://dx.doi.org/10.1175/mwr-d-14-00182.1.

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Abstract Ensemble square root filters can either assimilate all observations that are available at a given time at once, or assimilate the observations in batches or one at a time. For large-scale models, the filters are typically applied with a localized analysis step. This study demonstrates that the interaction of serial observation processing and localization can destabilize the analysis process, and it examines under which conditions the instability becomes significant. The instability results from a repeated inconsistent update of the state error covariance matrix that is caused by the l
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Huang, Bo, Xuguang Wang, and Craig H. Bishop. "The High-Rank Ensemble Transform Kalman Filter." Monthly Weather Review 147, no. 8 (2019): 3025–43. http://dx.doi.org/10.1175/mwr-d-18-0210.1.

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Abstract The ensemble Kalman filter is typically implemented either by applying the localization on the background error covariance matrix (B-localization) or by inflating the observation error variances (R-localization). A mathematical demonstration suggests that for the same effective localization function, the background error covariance matrix from the B-localization method shows a higher rank than the R-localization method. The B-localization method is realized in the ensemble transform Kalman filter (ETKF) by extending the background ensemble perturbations through modulation (MP-localiza
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Bergeron, Jean, Robert Leconte, Mélanie Trudel, and Sepehr Farhoodi. "On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling." Hydrology 8, no. 1 (2021): 36. http://dx.doi.org/10.3390/hydrology8010036.

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An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact on the performance of the data assimilation method. Many metrics can be used to calibrate these hyper-parameters but may not all yield the same optimal set of values. The current study investigated the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge fore
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Bishop, Craig H., Bo Huang, and Xuguang Wang. "A Nonvariational Consistent Hybrid Ensemble Filter." Monthly Weather Review 143, no. 12 (2015): 5073–90. http://dx.doi.org/10.1175/mwr-d-14-00391.1.

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Abstract A consistent hybrid ensemble filter (CHEF) for using hybrid forecast error covariance matrices that linearly combine aspects of both climatological and flow-dependent matrices within a nonvariational ensemble data assimilation scheme is described. The CHEF accommodates the ensemble data assimilation enhancements of (i) model space ensemble covariance localization for satellite data assimilation and (ii) Hodyss’s method for improving accuracy using ensemble skewness. Like the local ensemble transform Kalman filter (LETKF), the CHEF is computationally scalable because it updates local p
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Etherton, Brian J. "Preemptive Forecasts Using an Ensemble Kalman Filter." Monthly Weather Review 135, no. 10 (2007): 3484–95. http://dx.doi.org/10.1175/mwr3480.1.

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Abstract An ensemble Kalman filter (EnKF) estimates the error statistics of a model forecast using an ensemble of model forecasts. One use of an EnKF is data assimilation, resulting in the creation of an increment to the first-guess field at the observation time. Another use of an EnKF is to propagate error statistics of a model forecast forward in time, such as is done for optimizing the location of adaptive observations. Combining these two uses of an ensemble Kalman filter, a “preemptive forecast” can be generated. In a preemptive forecast, the increment to the first-guess field is, using e
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Zhou, Haiyan, Liangping Li, and J. Jaime Gómez-Hernández. "Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter." Abstract and Applied Analysis 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/805707.

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The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observ
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Potthast, Roland, Anne Walter, and Andreas Rhodin. "A Localized Adaptive Particle Filter within an Operational NWP Framework." Monthly Weather Review 147, no. 1 (2019): 345–62. http://dx.doi.org/10.1175/mwr-d-18-0028.1.

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Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows clos
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Delijani, Ebrahim Biniaz, Mahmoud Reza Pishvaie, and Ramin Bozorgmehry Boozarjomehry. "Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding." Advances in Water Resources 69 (July 2014): 181–96. http://dx.doi.org/10.1016/j.advwatres.2014.04.011.

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Chen, Yan, Weimin Zhang, and Mengbin Zhu. "A localized weighted ensemble Kalman filter for high‐dimensional systems." Quarterly Journal of the Royal Meteorological Society 146, no. 726 (2019): 438–53. http://dx.doi.org/10.1002/qj.3685.

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10

Auligné, Thomas, Benjamin Ménétrier, Andrew C. Lorenc, and Mark Buehner. "Ensemble–Variational Integrated Localized Data Assimilation." Monthly Weather Review 144, no. 10 (2016): 3677–96. http://dx.doi.org/10.1175/mwr-d-15-0252.1.

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Hybrid variational–ensemble data assimilation (hybrid DA) is widely used in research and operational systems, and it is considered the current state of the art for the initialization of numerical weather prediction models. However, hybrid DA requires a separate ensemble DA to estimate the uncertainty in the deterministic variational DA, which can be suboptimal both technically and scientifically. A new framework called the ensemble–variational integrated localized (EVIL) data assimilation addresses this inconvenience by updating the ensemble analyses using information from the variational dete
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Dissertations / Theses on the topic "Ensemble Kalman filter hyper-localized"

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Villanueva, Lucas. "Développement d’outils d’assimilation de données pour l’estimation augmentée d’écoulements internes." Electronic Thesis or Diss., Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2024. http://www.theses.fr/2024ESMA0019.

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Dans ce travail de thèse, des outils d’assimilation de données sont utilisés pour augmenter les performances de solveurs de mécanique des fluides dédiés à la simulation des grandes échelles. L’objectif est d’améliorer la prédiction et l’étude d’évènements marginaux nuisibles à l’intégrité des systèmes physiques. Bien que difficile à caractériser et à modéliser, la compréhension détaillée de ces phénomènes physiques complexes est essentielle pour le développement d’applications plus durables. Ces objectifs s’intègrent aux activités de recherche du projet ANR ALEKCIA dans lequel ce travail de th
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Conference papers on the topic "Ensemble Kalman filter hyper-localized"

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Yuan, Yufei, Friso Scholten, and Hans Van Lint. "Efficient Traffic State Estimation and Prediction Based on the Ensemble Kalman Filter with a Fast Implementation and Localized Deterministic Scheme." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.85.

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