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 323 articles , 1 standard article )

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  1. Bishop, Adrian N.; Del Moral, Pierre; Niclas, Angèle: A perturbation analysis of stochastic matrix Riccati diffusions (2020)
  2. Chada, Neil K.; Stuart, Andrew M.; Tong, Xin T.: Tikhonov regularization within ensemble Kalman inversion (2020)
  3. Crisan, Dan; López-Yela, Alberto; Miguez, Joaquin: Stable approximation schemes for optimal filters (2020)
  4. da Silva, Andre F. C.; Colonius, Tim: Flow state estimation in the presence of discretization errors (2020)
  5. de Wiljes, Jana; Pathiraja, Sahani; Reich, Sebastian: Ensemble transform algorithms for nonlinear smoothing problems (2020)
  6. Ganguli, R.; Adhikari, S.: The digital twin of discrete dynamic systems: initial approaches and future challenges (2020)
  7. Garbuno-Inigo, Alfredo; Hoffmann, Franca; Li, Wuchen; Stuart, Andrew M.: Interacting Langevin diffusions: gradient structure and ensemble Kalman sampler (2020)
  8. Hasegawa, Takanori; Yamaguchi, Rui; Niida, Atsushi; Miyano, Satoru; Imoto, Seiya: Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model (2020)
  9. He, Qizhi; Chen, Jiun-Shyan: A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (2020)
  10. Stabile, Giovanni; Rosic, Bojana: Bayesian identification of a projection-based reduced order model for computational fluid dynamics (2020)
  11. Zhang, Xinlei; Xiao, Heng; Gomez, Thomas; Coutier-Delgosha, Olivier: Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes (2020)
  12. Albers, David J.; Blancquart, Paul-Adrien; Levine, Matthew E.; Esmaeilzadeh Seylabi, Elnaz; Stuart, Andrew: Ensemble Kalman methods with constraints (2019)
  13. 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)
  14. Bergou, El Houcine; Gratton, Serge; Mandel, Jan: On the convergence of a non-linear ensemble Kalman smoother (2019)
  15. Bishop, Adrian N.; Del Moral, Pierre: Stability properties of systems of linear stochastic differential equations with random coefficients (2019)
  16. Bishop, A. N.; Del Moral, P.; Kamatani, K.; Rémillard, B.: On one-dimensional Riccati diffusions (2019)
  17. Blömker, Dirk; Schillings, Claudia; Wacker, Philipp; Weissmann, Simon: Well posedness and convergence analysis of the ensemble Kalman inversion (2019)
  18. Chen, Nan; Majda, Andrew J.; Tong, Xin T.: Spatial localization for nonlinear dynamical stochastic models for excitable media (2019)
  19. Evensen, Geir: Accounting for model errors in iterative ensemble smoothers (2019)
  20. Hoang, H. S.; Baraille, Remy: A simple numerical method based simultaneous stochastic perturbation for estimation of high dimensional matrices (2019)

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