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 346 articles , 1 standard article )

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  1. Alfonzo, Miguel; Oliver, Dean S.: Seismic data assimilation with an imperfect model (2020)
  2. Bishop, Adrian N.; Del Moral, Pierre; Niclas, Angèle: A perturbation analysis of stochastic matrix Riccati diffusions (2020)
  3. Bulté, Matthieu; Latz, Jonas; Ullmann, Elisabeth: A practical example for the non-linear Bayesian filtering of model parameters (2020)
  4. Chada, Neil K.; Stuart, Andrew M.; Tong, Xin T.: Tikhonov regularization within ensemble Kalman inversion (2020)
  5. Chen, Nan: Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques (2020)
  6. Chen, Nan; Majda, Andrew J.: Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations (2020)
  7. Chen, Nan; Majda, Andrew J.: Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series (2020)
  8. Cotter, Colin; Crisan, Dan; Holm, Darryl D.; Pan, Wei; Shevchenko, Igor: A particle filter for stochastic advection by Lie transport: a case study for the damped and forced incompressible two-dimensional Euler equation (2020)
  9. Crisan, Dan; López-Yela, Alberto; Miguez, Joaquin: Stable approximation schemes for optimal filters (2020)
  10. da Silva, Andre F. C.; Colonius, Tim: Flow state estimation in the presence of discretization errors (2020)
  11. de Moraes, Rafael J.; Hajibeygi, Hadi; Jansen, Jan Dirk: A multiscale method for data assimilation (2020)
  12. de Wiljes, Jana; Pathiraja, Sahani; Reich, Sebastian: Ensemble transform algorithms for nonlinear smoothing problems (2020)
  13. de Wiljes, Jana; Tong, Xin T.: Analysis of a localised nonlinear ensemble Kalman Bucy filter with complete and accurate observations (2020)
  14. Ganguli, R.; Adhikari, S.: The digital twin of discrete dynamic systems: initial approaches and future challenges (2020)
  15. Gao, Guohua; Jiang, Hao; Vink, Jeroen C.; Chen, Chaohui; El Khamra, Yaakoub; Ita, Joel J.: Gaussian mixture model fitting method for uncertainty quantification by conditioning to production data (2020)
  16. Garbuno-Inigo, Alfredo; Hoffmann, Franca; Li, Wuchen; Stuart, Andrew M.: Interacting Langevin diffusions: gradient structure and ensemble Kalman sampler (2020)
  17. Golmohammadi, Azarang; Jafarpour, Behnam: Reducing uncertainty in conceptual prior models of complex geologic systems via integration of flow response data (2020)
  18. Hasegawa, Takanori; Yamaguchi, Rui; Niida, Atsushi; Miyano, Satoru; Imoto, Seiya: Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model (2020)
  19. He, Qizhi; Chen, Jiun-Shyan: A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (2020)
  20. Holm, Håvard H.; Sætra, Martin L.; Brodtkorb, André R.: Data assimilation for ocean drift trajectories using massive ensembles and GPUs (2020)

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