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 307 articles , 1 standard article )

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  1. de Wiljes, Jana; Pathiraja, Sahani; Reich, Sebastian: Ensemble transform algorithms for nonlinear smoothing problems (2020)
  2. Albers, David J.; Blancquart, Paul-Adrien; Levine, Matthew E.; Esmaeilzadeh Seylabi, Elnaz; Stuart, Andrew: Ensemble Kalman methods with constraints (2019)
  3. Albers, David J.; Levine, Matthew E.; Mamykina, Lena; Hripcsak, George: The parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems (2019)
  4. Bergou, El Houcine; Gratton, Serge; Mandel, Jan: On the convergence of a non-linear ensemble Kalman smoother (2019)
  5. Bishop, Adrian N.; Del Moral, Pierre: Stability properties of systems of linear stochastic differential equations with random coefficients (2019)
  6. Bishop, A. N.; Del Moral, P.; Kamatani, K.; Rémillard, B.: On one-dimensional Riccati diffusions (2019)
  7. Blömker, Dirk; Schillings, Claudia; Wacker, Philipp; Weissmann, Simon: Well posedness and convergence analysis of the ensemble Kalman inversion (2019)
  8. Chen, Nan; Majda, Andrew J.; Tong, Xin T.: Spatial localization for nonlinear dynamical stochastic models for excitable media (2019)
  9. Evensen, Geir: Accounting for model errors in iterative ensemble smoothers (2019)
  10. Hoang, H. S.; Baraille, Remy: A simple numerical method based simultaneous stochastic perturbation for estimation of high dimensional matrices (2019)
  11. Hou, Thomas Y.; Lam, Ka Chun; Zhang, Pengchuan; Zhang, Shumao: Solving Bayesian inverse problems from the perspective of deep generative networks (2019)
  12. Jahanbakhshi, Reza; Zaki, Tamer A.: Nonlinearly most dangerous disturbance for high-speed boundary-layer transition (2019)
  13. Kelly, David; Stuart, Andrew M.: Ergodicity and accuracy of optimal particle filters for Bayesian data assimilation (2019)
  14. Kovachki, Nikola B.; Stuart, Andrew M.: Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (2019)
  15. Kumar, Devesh; Srinivasan, Sanjay: Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics (2019)
  16. Ma, Xiang; Bi, Linfeng: A robust adaptive iterative ensemble smoother scheme for practical history matching applications (2019)
  17. Ng, Michael K.; Zhu, Zhaochen: Sparse matrix computation for air quality forecast data assimilation (2019)
  18. Nino-Ruiz, Elias David: Non-linear data assimilation via trust region optimization (2019)
  19. Pathiraja, Sahani; Reich, Sebastian: Discrete gradients for computational Bayesian inference (2019)
  20. Reich, Sebastian: Data assimilation: the Schrödinger perspective (2019)

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