EnKF

EnKF-The Ensemble Kalman Filter The EnKF is a sophisticated sequental data assimilation method. It applies an ensemble of model states to represent the error statistics of the model estimate, it applies ensemble integrations to predict the error statistics forward in time, and it uses an analysis scheme which operates directly on the ensemble of model states when observations are assimilated. The EnKF has proven to efficiently handle strongly nonlinear dynamics and large state spaces and is now used in realistic applications with primitive equation models for the ocean and atmosphere. A recent article in the Siam News Oct. 2003 by Dana McKenzie suggests that the killer heat wave that hit Central Europe in the summer 2003 could have been more efficiently forecast if the EnKF had been used by Meteorological Centers. See the article ”Ensemble Kalman Filters Bring Weather Models Up to Date” on http://www.siam.org/siamnews/10-03/tococt03.htm This page is established as a reference page for users of the EnKF, and it contains documentation, example codes, and standardized Fortran 90 subroutines which can be used in new implementations of the EnKF. The material on this page will provide new users of the EnKF with a quick start and spinup, and experienced users with optimized code which may increase the performence of their implementations.


References in zbMATH (referenced in 273 articles , 1 standard article )

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  1. Bergou, El Houcine; Gratton, Serge; Mandel, Jan: On the convergence of a non-linear ensemble Kalman smoother (2019)
  2. Bishop, Adrian N.; Del Moral, Pierre: Stability properties of systems of linear stochastic differential equations with random coefficients (2019)
  3. Hoang, H. S.; Baraille, Remy: A simple numerical method based simultaneous stochastic perturbation for estimation of high dimensional matrices (2019)
  4. Kumar, Devesh; Srinivasan, Sanjay: Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics (2019)
  5. Ng, Michael K.; Zhu, Zhaochen: Sparse matrix computation for air quality forecast data assimilation (2019)
  6. Bishop, Adrian N.; Del Moral, Pierre: On the robustness of Riccati flows to complete model misspecification (2018)
  7. Bishop, Adrian N.; Del Moral, Pierre; Pathiraja, Sahani D.: Perturbations and projections of Kalman-Bucy semigroups (2018)
  8. Branicki, M.; Majda, A. J.; Law, K. J. H.: Accuracy of some approximate Gaussian filters for the Navier-Stokes equation in the presence of model error (2018)
  9. Carneiro, João; Azevedo, Leonardo; Pereira, Maria: High-dimensional geostatistical history matching, vectorial multi-objective geostatistical history matching of oil reservoirs and uncertainty assessment (2018)
  10. Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.: Parameterizations for ensemble Kalman inversion (2018)
  11. de Wiljes, Jana; Reich, Sebastian; Stannat, Wilhelm: Long-time stability and accuracy of the ensemble Kalman-Bucy filter for fully observed processes and small measurement noise (2018)
  12. D’Oria, Marco; Zanini, Andrea; Cupola, Fausto: Oscillatory pumping test to estimate aquifer hydraulic parameters in a Bayesian geostatistical framework (2018)
  13. Evensen, Geir: Analysis of iterative ensemble smoothers for solving inverse problems (2018)
  14. Evensen, Geir; Eikrem, Kjersti Solberg: Conditioning reservoir models on rate data using ensemble smoothers (2018)
  15. Farhat, Aseel; Johnston, Hans; Jolly, Michael; Titi, Edriss S.: Assimilation of nearly turbulent Rayleigh-Bénard flow through vorticity or local circulation measurements: a computational study (2018)
  16. Gervais, Véronique; Le Ravalec, Mickaële: Identifying influence areas with connectivity analysis -- application to the local perturbation of heterogeneity distribution for history matching (2018)
  17. Grudzien, Colin; Carrassi, Alberto; Bocquet, Marc: Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error (2018)
  18. Iglesias, Marco; Park, Minho; Tretyakov, M. V.: Bayesian inversion in resin transfer molding (2018)
  19. Iglesias, Marco; Sawlan, Zaid; Scavino, Marco; Tempone, Raúl; Wood, Christopher: Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: application to heat transfer in building walls (2018)
  20. Jang, Junyoung; Jang, Kihoon; Kwon, Hee-Dae; Lee, Jeehyun: Feedback control of an HBV model based on ensemble Kalman filter and differential evolution (2018)

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