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 213 articles , 1 standard article )

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  1. Nino-Ruiz, Elias D.; Sandu, Adrian; Deng, Xinwei: An ensemble Kalman filter implementation based on modified Cholesky decomposition for inverse covariance matrix estimation (2018)
  2. Yang, Chao; Kumar, Mrinal: On the effectiveness of Monte Carlo for initial uncertainty forecasting in nonlinear dynamical systems (2018)
  3. Acevedo, Walter; de Wiljes, Jana; Reich, Sebastian: Second-order accurate ensemble transform particle filters (2017)
  4. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  5. Belyaev, K.P.; Kuleshov, A.A.; Smirnov, I.N.; Tanajura, C.A.S.: Parallel assimilation of observed data in the hydrodynamic model of the Ocean circulation (2017)
  6. Bishop, Adrian N.; Del Moral, Pierre: On the stability of Kalman-Bucy diffusion processes (2017)
  7. Bocquet, Marc; Gurumoorthy, Karthik S.; Apte, Amit; Carrassi, Alberto; Grudzien, Colin; Jones, Christopher K.R.T.: Degenerate Kalman filter error covariances and their convergence onto the unstable subspace (2017)
  8. Bröcker, Jochen: Existence and uniqueness for four-dimensional variational data assimilation in discrete time (2017)
  9. Chen, Yan; Oliver, Dean S.: Localization and regularization for iterative ensemble smoothers (2017)
  10. del Moral, Pierre; Kurtzmann, Aline; Tugaut, Julian: On the stability and the uniform propagation of chaos of a class of extended ensemble Kalman-Bucy filters (2017)
  11. Gilbert, R.C.; Trafalis, T.B.; Richman, M.B.; Leslie, L.M.: A data-driven kernel method assimilation technique for geophysical modelling (2017)
  12. Gurumoorthy, Karthik S.; Grudzien, Colin; Apte, Amit; Carrassi, Alberto; Jones, Christopher K.R.T.: Rank deficiency of Kalman error covariance matrices in linear time-varying system with deterministic evolution (2017)
  13. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  14. Hickmann, Kyle S.; Godinez, Humberto C.: A multiresolution ensemble Kalman filter using the wavelet decomposition (2017)
  15. Hoang, Hong Son; Baraille, Remy: On the efficient low cost procedure for estimation of high-dimensional prediction error covariance matrices (2017)
  16. McDougall, D.; Moore, R.O.: Optimal strategies for the control of autonomous vehicles in data assimilation (2017)
  17. Meldi, M.; Poux, A.: A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows (2017)
  18. Quarteroni, A.; Manzoni, A.; Vergara, C.: The cardiovascular system: mathematical modelling, numerical algorithms and clinical applications (2017)
  19. Robert, Sylvain; Künsch, Hans R.: Localization in high-dimensional Monte Carlo filtering (2017)
  20. Rosenthal, W.Steven; Venkataramani, Shankar; Mariano, Arthur J.; Restrepo, Juan M.: Displacement data assimilation (2017)

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