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 238 articles , 1 standard article )

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  1. Bishop, Adrian N.; Del Moral, Pierre; Pathiraja, Sahani D.: Perturbations and projections of Kalman-Bucy semigroups (2018)
  2. Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.: Parameterizations for ensemble Kalman inversion (2018)
  3. 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)
  4. Iglesias, Marco; Park, Minho; Tretyakov, M. V.: Bayesian inversion in resin transfer molding (2018)
  5. 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)
  6. Llopis, Francesc Pons; Kantas, Nikolas; Beskos, Alexandros; Jasra, Ajay: Particle filtering for stochastic Navier-Stokes signal observed with linear additive noise (2018)
  7. Nino-Ruiz, Elias D.; Sandu, Adrian; Deng, Xinwei: An ensemble Kalman filter implementation based on modified Cholesky decomposition for inverse covariance matrix estimation (2018)
  8. Qiao, Huijie; Zhang, Yanjie; Duan, Jinqiao: Effective filtering on a random slow manifold (2018)
  9. Schillings, C.; Stuart, A. M.: Convergence analysis of ensemble Kalman inversion: the linear, noisy case (2018)
  10. Subber, Waad; Sarkar, Abhijit: A parallel time integrator for noisy nonlinear oscillatory systems (2018)
  11. Tong, Xin T.: Performance analysis of local ensemble Kalman filter (2018)
  12. Yang, Chao; Kumar, Mrinal: On the effectiveness of Monte Carlo for initial uncertainty forecasting in nonlinear dynamical systems (2018)
  13. Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel: A unifying view of synchronization for data assimilation in complex nonlinear networks (2017)
  14. Acevedo, Walter; de Wiljes, Jana; Reich, Sebastian: Second-order accurate ensemble transform particle filters (2017)
  15. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  16. 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)
  17. Bishop, Adrian N.; Del Moral, Pierre: On the stability of Kalman-Bucy diffusion processes (2017)
  18. 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)
  19. Bröcker, Jochen: Existence and uniqueness for four-dimensional variational data assimilation in discrete time (2017)
  20. Chen, Yan; Oliver, Dean S.: Localization and regularization for iterative ensemble smoothers (2017)

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