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 385 articles , 1 standard article )

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  1. Bishop, Adrian N.; Del Moral, Pierre: An explicit Floquet-type representation of Riccati aperiodic exponential semigroups (2021)
  2. Chernov, Alexey; Hoel, Håkon; Law, Kody J. H.; Nobile, Fabio; Tempone, Raul: Multilevel ensemble Kalman filtering for spatio-temporal processes (2021)
  3. Ding, Zhiyan; Li, Qin: Ensemble Kalman sampler: mean-field limit and convergence analysis (2021)
  4. Ding, Zhiyan; Li, Qin: Ensemble Kalman inversion: mean-field limit and convergence analysis (2021)
  5. Evensen, Geir: Formulating the history matching problem with consistent error statistics (2021)
  6. Korn, Peter: Strong solvability of a variational data assimilation problem for the primitive equations of large-scale atmosphere and Ocean dynamics (2021)
  7. Lin, Jing; Lermusiaux, Pierre F. J.: Minimum-correction second-moment matching: theory, algorithms and applications (2021)
  8. Loe, Margrethe Kvale; Grana, Dario; Tjelmeland, Håkon: Geophysics-based fluid-facies predictions using ensemble updating of binary state vectors (2021)
  9. Lopez-Restrepo, Santiago; Nino-Ruiz, Elias D.; Guzman-Reyes, Luis G.; Yarce, Andres; Quintero, O. L.; Pinel, Nicolas; Segers, Arjo; Heemink, A. W.: An efficient ensemble Kalman filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge (2021)
  10. Luo, Xiaodong: Novel iterative ensemble smoothers derived from a class of generalized cost functions (2021)
  11. Marcotte, Christopher D.; Fenton, Flavio H.; Hoffman, Matthew J.; Cherry, Elizabeth M.: Robust data assimilation with noise: applications to cardiac dynamics (2021)
  12. Moyen, Rémi; Gentilhomme, Théophile: Adaptive ensemble-based optimisation for petrophysical inversion (2021)
  13. Nino-Ruiz, Elias D.; Guzman, Luis; Jabba, Daladier: An ensemble Kalman filter implementation based on the Ledoit and Wolf covariance matrix estimator (2021)
  14. Otkin, Jason A.; Potthast, Roland W. E.; Lawless, Amos S.: Nonlinear conditional model bias estimation for data assimilation (2021)
  15. Panda, Nishant; Fernández-Godino, M. Giselle; Godinez, Humberto C.; Dawson, Clint: A data-driven non-linear assimilation framework with neural networks (2021)
  16. Popov, Andrey A.; Mou, Changhong; Sandu, Adrian; Iliescu, Traian: A multifidelity ensemble Kalman filter with reduced order control variates (2021)
  17. Rammay, Muzammil Hussain; Elsheikh, Ahmed H.; Chen, Yan: Flexible iterative ensemble smoother for calibration of perfect and imperfect models (2021)
  18. Reich, Sebastian; Weissmann, Simon: Fokker-Planck particle systems for Bayesian inference: computational approaches (2021)
  19. Vasilyeva, Maria; Tyrylgin, Aleksei; Brown, Donald L.; Mondal, Anirban: Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique (2021)
  20. Wang, Guangyao; Pan, Yulin: Phase-resolved ocean wave forecast with ensemble-based data assimilation (2021)

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