Literatura científica selecionada sobre o tema "Ensemble Kalman filter hyper-localized"
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Artigos de revistas sobre o assunto "Ensemble Kalman filter hyper-localized"
Nerger, Lars. "On Serial Observation Processing in Localized Ensemble Kalman Filters". Monthly Weather Review 143, n.º 5 (1 de maio de 2015): 1554–67. http://dx.doi.org/10.1175/mwr-d-14-00182.1.
Texto completo da fonteHuang, Bo, Xuguang Wang e Craig H. Bishop. "The High-Rank Ensemble Transform Kalman Filter". Monthly Weather Review 147, n.º 8 (31 de julho de 2019): 3025–43. http://dx.doi.org/10.1175/mwr-d-18-0210.1.
Texto completo da fonteBergeron, Jean, Robert Leconte, Mélanie Trudel e 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, n.º 1 (24 de fevereiro de 2021): 36. http://dx.doi.org/10.3390/hydrology8010036.
Texto completo da fonteBishop, Craig H., Bo Huang e Xuguang Wang. "A Nonvariational Consistent Hybrid Ensemble Filter". Monthly Weather Review 143, n.º 12 (1 de dezembro de 2015): 5073–90. http://dx.doi.org/10.1175/mwr-d-14-00391.1.
Texto completo da fonteEtherton, Brian J. "Preemptive Forecasts Using an Ensemble Kalman Filter". Monthly Weather Review 135, n.º 10 (1 de outubro de 2007): 3484–95. http://dx.doi.org/10.1175/mwr3480.1.
Texto completo da fonteZhou, Haiyan, Liangping Li e 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.
Texto completo da fontePotthast, Roland, Anne Walter e Andreas Rhodin. "A Localized Adaptive Particle Filter within an Operational NWP Framework". Monthly Weather Review 147, n.º 1 (janeiro de 2019): 345–62. http://dx.doi.org/10.1175/mwr-d-18-0028.1.
Texto completo da fonteDelijani, Ebrahim Biniaz, Mahmoud Reza Pishvaie e Ramin Bozorgmehry Boozarjomehry. "Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding". Advances in Water Resources 69 (julho de 2014): 181–96. http://dx.doi.org/10.1016/j.advwatres.2014.04.011.
Texto completo da fonteChen, Yan, Weimin Zhang e Mengbin Zhu. "A localized weighted ensemble Kalman filter for high‐dimensional systems". Quarterly Journal of the Royal Meteorological Society 146, n.º 726 (15 de dezembro de 2019): 438–53. http://dx.doi.org/10.1002/qj.3685.
Texto completo da fonteAuligné, Thomas, Benjamin Ménétrier, Andrew C. Lorenc e Mark Buehner. "Ensemble–Variational Integrated Localized Data Assimilation". Monthly Weather Review 144, n.º 10 (outubro de 2016): 3677–96. http://dx.doi.org/10.1175/mwr-d-15-0252.1.
Texto completo da fonteTeses / dissertações sobre o assunto "Ensemble Kalman filter hyper-localized"
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.
Texto completo da fonteIn this thesis, data assimilation tools are used to increase the performance of fluid mechanics solvers dedicated to large eddy simulations. The aim is to improve the prediction and study of marginal events harmful to the integrity of physical systems. Although difficult to characterize and model, a detailed understanding of these complex physical phenomena is essential for the development of more sustainable applications. These objectives are in line with the research activities of the ANR ALEKCIA project, of which this thesis is a part. More specifically, the aim is to meet the need to couple numerical fluid mechanics calculations with a sequential data assimilation algorithm. CONES (Coupling OpenFOAM with Numerical EnvironmentS) tool has been developed to provide an answer to this need by using the OpenFOAM software and the ensemble Kalman filter. The latter can be used both to calibrate the physical parameters of the numerical simulation and to infer physical fields, such as the velocity field. CONES is used to infer three case studies of increasing complexity. The first optimizes the closure coefficients of RANS-type turbulence models for an incompressible flow by assimilating experimental data. The calibration of these parameters leads in particular to a topological improvement in the recirculation structures of the geometry. The case also demonstrates the importance of the quality of the heterogeneous source of information observed rather than its quantity. In a second study, large eddy simulation is used to provide a prediction of the unsteady three-dimensional characteristics of a turbulent incompressible flow in a channel. In addition to optimizing the Smagorinsky model, the velocity field is partially synchronized with the observed data to facilitate the reconstruction of the unsteady structures. The influence of hyper parameters such as inflation is highlighted. Finally, a variant of the Kalman algorithm, the hyper-localized ensemble Kalman filter, is developed for the last case study. In particular, this method reduces the computational cost.It is used to infer a LES of the compressible flow of a simplified engine geometry. The pulsed reference input condition is correctly calibrated and the velocity field of the inferred simulations is locally synchronized. The correction provided by the algorithm also shows an improvement in the energy distribution of the inferred region in line with the reference distribution. In conclusion, the potential of the ensemble Kalman filter for calibrating physical parameters and reconstructing local structures by observing high-fidelity data from the real system has been demonstrated. This could enable the study of extreme events that could damage the integrity of the physical system, thanks to the augmented numerical simulation of these phenomena
Trabalhos de conferências sobre o assunto "Ensemble Kalman filter hyper-localized"
Yuan, Yufei, Friso Scholten e 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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