State and parameter estimation in stochastic dynamical models

Physica D: Nonlinear Phenomena

Volumn 239, Issue 18, 2010, Pages 1781-1788

  Delsole, Timothy ^a   Yang, Xiaosong ^a  

^a CENTER FOR OCEAN LAND ATMOSPHERE STUDIES   (United States)

Author keywords

Data assimilation; Ensemble Kalman Filter; Maximum likelihood; Stochastic parameter estimation

Indexed keywords

KALMAN FILTERS; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; STOCHASTIC SYSTEMS;

DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; GENERALIZED MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATE; SQUARE ROOT KALMAN FILTER; STATE AND PARAMETER ESTIMATIONS; STOCHASTIC DYNAMICAL MODEL; STOCHASTIC PARAMETERS;

STOCHASTIC MODELS;

EID: 77955465991     PISSN: 01672789     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physd.2010.06.001     Document Type: Article

References (22)

1. J. Carrera, and S.P. Neuman Estimation of aquifer parameters under transient and steady state conditions: 1. maximum likelihood method incorporating prior information Water Resour. Res. 22 2 1986 199 210 (Pubitemid 16477839)
2. A.F. Bennett Inverse Methods in Physical Oceanography 1992 Cambridge University Press
3. D. Dochain State and parameter estimation in chemical and biochemical processes: a tutorial J. Process Control 13 2003 801 818
4. X.-M. Hu, F. Zhang, and J.W. Nielsen-Gammon Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: a real-data study Geophys. Res. Lett. 37 2010 10.1029/2010GL043017 L08802
5. J.L. Anderson An ensemble adjustment filter for data assimilation Mon. Weather Rev. 129 2001 2884 2903
6. S.-J. Baek, B.R. Hunt, E. Kalney, E. Ott, and I. Szunyogh Local ensemble Kalman filtering in the presence of model bias Tellus 58A 2006 293 306
7. A. Gelb, Applied Optimal Estimation 1974 MIT Press Cambridge, MA
8. A.H. Jazwinski Stochastic Processes and Filtering Theory 1970 Academic Press New York p. 376
9. D. Zupanski, and M. Zupanski Model error estimation employing an ensemble data assimilation approach Mon. Weather Rev. 134 2006 1337 1354
10. X. Yang, and T. DelSole Using the ensemble Kalman Filter to estimate multiplicative model parameters Tellus 61A 2009 601 609
11. J. Berner, F.J. Doblas-Reyes, T.N. Palmer, G. Shutts, and A. Weisheimer Impact of a quasi-stochastic cellular automation backsctter scheme on the systematic error and seasonal prediction skill of a global climate model Phil. Trans. R. Soc. A 366 2008 2559 2577
12. J. Berner, G.J. Shutts, M. Leutbecher, and T.N. Palmer A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system J. Atmos. Sci. 66 2009 603 626
13. G.J. Shutts A kinetic energy backsctter algorithm for use in ensemble prediction systems Quart. J. Roy. Meteor. Soc. 131 2005 3079 3102
14. J.O. Berger Statistical Decision Theory and Bayesian Analysis 2nd ed. 1985 Springer
15. I. Navon Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography Dyn. Atmos. Oceans 27 1997 55 77
16. P.S. Maybeck Stochastic Models, Estimation, and Control. Vol. 1 1982 Academic Press
17. J. Whitaker, and T.M. Hamill Ensemble data assimilation without perturbed observations Mon. Weather Rev. 130 2002 1913 1924
18. J.A. Hansen, and C. Penland Efficient approximation technigues for integrating numerical stochastic differential equations Mon. Weather Rev. 134 2006 3006 3014
19. C.W. Gardiner Handbook of Stochastic Models 2nd ed. 1990 Springer-Verlag
20. J.A. Hansen, and C. Penland On stochastic parameter estimation using data assimilation Physica D 230 2007 88 98
21. T.M. Hamill Interpretation of rank histogram for verifying ensemble forecasts Mon. Weather Rev. 129 2001 550 560
22. I.T. Jolliffe, and C. Primo Evaluating rank histograms using decompositions of the chi-square test statistic Mon. Weather Rev. 136 2008 2133 2139