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 172 articles , 1 standard article )

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  1. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  2. 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)
  3. Bröcker, Jochen: Existence and uniqueness for four-dimensional variational data assimilation in discrete time (2017)
  4. 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)
  5. Gilbert, R.C.; Trafalis, T.B.; Richman, M.B.; Leslie, L.M.: A data-driven kernel method assimilation technique for geophysical modelling (2017)
  6. 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)
  7. Schillings, Claudia; Stuart, Andrew M.: Analysis of the ensemble Kalman filter for inverse problems (2017)
  8. Su, Chen; Tu, Xuemin: Sequential implicit sampling methods for Bayesian inverse problems (2017)
  9. Sun, Wenyue; Durlofsky, Louis J.: A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems (2017)
  10. Ananthasayanam, M.R.; Mohan, M.Shyam; Naik, Naren; Gemson, R.M.O.: A heuristic reference recursive recipe for adaptively tuning the Kalman filter statistics. I: Formulation and simulation studies (2016)
  11. Barber, Jared; Tanase, Roxana; Yotov, Ivan: Kalman filter parameter estimation for a nonlinear diffusion model of epithelial cell migration using stochastic collocation and the Karhunen-Loeve expansion (2016)
  12. Belyaev, Konstantin P.; Kuleshov, Andrey A.; Tanajura, Clemente A.S.: An application of a data assimilation method based on the diffusion stochastic process theory using altimetry data in Atlantic (2016)
  13. Belyaev, K.P.; Kuleshov, A.A.; Tuchkova, N.P.; Tanajura, C.A.S.: On asymptotic distributions of analysis characteristics for the linear data assimilation problem (2016)
  14. Bergou, E.; Gratton, S.; Vicente, L.N.: Levenberg-Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation (2016)
  15. Chustagulprom, Nawinda; Reich, Sebastian; Reinhardt, Maria: A hybrid ensemble transform particle filter for nonlinear and spatially extended dynamical systems (2016)
  16. Hoel, Håkon; Law, Kody J.H.; Tempone, Raul: Multilevel ensemble Kalman filtering (2016)
  17. Iglesias, Marco A.: A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems (2016)
  18. Kleppe, Tore Selland; Skaug, Hans J.: Bandwidth selection in pre-smoothed particle filters (2016)
  19. Mons, V.; Chassaing, J.-C.; Gomez, T.; Sagaut, P.: Reconstruction of unsteady viscous flows using data assimilation schemes (2016)
  20. Nino Ruiz, Elias D.; Sandu, Adrian: A derivative-free trust region framework for variational data assimilation (2016)

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