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Data assimilation with the weighted ensemble Kalman filter Cover

Data assimilation with the weighted ensemble Kalman filter

Open Access
|Jan 2010

References

  1. Anderson , J . 2001 . An ensemble adjustment Kalman filter for data assimilation . Mon. Wea. Rev . 129 ( 12 ), 2884 2903 .
  2. Anderson , J . 2007 . An adaptive covariance inflation error correction algorithm for ensemble filters . Tellus 59A , 210 224 .
  3. Anderson , J. and Anderson , S . 1999 . A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts . Mon. Wea. Rev . 127 ( 12 ), 2741 2758 .
  4. Anderson , J. L . 2003 . A local least squares framework for ensemble filtering . Mon. Wea. Rev . 131 ( 4 ), 634 642 .
  5. Arnaud , E. and Mémin , E . 2007 . Partial linear gaussian model for tracking in image sequences using sequential monte carlo methods . Int. J. Comput. Vision 74 ( 1 ), 75 102 .
  6. Arulampalam , M. , Maskell , S. , Gordon , N. and Clapp , T . 2002 . A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking . IEEE Trans. Signal Process . 50 ( 2 ).
  7. Bennet , A . 1992 . Inverse Methods in Physical Oceanography . Cambridge University Press, New York , NY .
  8. Bertino , L. , Evensen , G. and Wackernagel , H . 2003 . Sequential data assimilation techniques in oceanography . Int. Statist. Rev . 71 ( 2 ), 223 241 .
  9. Bishop , C. , Etherton , B. and Majumdar , S . 2001 . Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects . Mon. Wea. Rev . 129 ( 3 ), 420 436 .
  10. Burgers , G. , van Leeuwen , P. and Evensen , G . 1998 . Analysis scheme in the ensemble Kalman filter. Mon . Wea. Re v . 126 , 1719 1724 .
  11. Corpetti , T. , Heas , R , Memin , E. and Papadalcis , N . 2009 . Pressure image asimilation for atmospheric motion estimation . Taus 61A , 160 178 .
  12. Courtier , P . 1997 . Dual formulation of four-dimensional variational assimilation . Quart. J. Roy. Meteorol. Soc . 123 , 2249 2461 .
  13. Courtier , R , Andersson , E. , Heckley , W. , Pailleux , J. , Vasiljevic , D. and co-authors . 1998. The ECMWF implementation of three-dimensional variational assimilation (3D-VAR). Part 1: formulation . Quart. J. Roy. Meteorol. Soc . 124 , 1783– 1807 .
  14. Crisan , D. and Doucet , A . 2002 . Survey of convergence results on particle filering methods for practitioners . IEEE Trans. Signal Process . 50 ( 3 ), 736 746 .
  15. Cuzol , A. and Memin , E . 2009 . A stochastic filter technique for fluid flows velocity fields tracking . IEEE Trans. Pattern Anal. Mach. Intell . 31 ( 7 ), 1278 1293 .
  16. Del Moral , P . 2004 . Feynman-Kac Formulae Genealogical and Interacting Particle Systems with Applications . Series: Probability and Applications . Springer , New York .
  17. Doucet , A. , Godsill , S. and Andrieu , C . 2000 . On sequential Monte Carlo sampling methods for Bayesian filtering . Stat. Comput . 10 ( 3 ), 197 208 .
  18. Elliot , E , Horntrop , D. and Majda , A . 1997 . A Fourier-wavelet Monte Carlo method for fractal random fields . J. Comput. Phys . 132 ( 2 ), 384 408 .
  19. Evensen , G . 1994 . Sequential data assimilation with a non linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res . 99 ( C5 )(10), 143 162 .
  20. Evensen , G . 2003 . The ensemble Kalman filter, theoretical formulation and practical implementation . Ocean Dyn . 53 ( 4 ), 343 367 .
  21. Evensen , G . 2006 . Data Assimilation: The Ensemble Kalman Filter . Springer-Verlag , New-york .
  22. Evensen , G. and van Leeuwen , P . 2000 . An ensemble Kalman smoother for nonlinear dynamics . Mon. Wea. Rev . 128 ( 6 ), 1852 1867 .
  23. Fertig , E. , Harlim , J. and Hunt , B . 2007 . A comparative study of 4D-VAR and a 4D ensemble Kalman filter. Perfect model simulations with lorenz-96 . Tellus 59A , 96 100 .
  24. Gauthier , R , Courtier , P. and Moll , P . 1993 . Assimilation of simulated wind lidar data with a Kalman filter . Mon. Wea. Rev . 121 ( 6 ), 1803 1820 .
  25. Gordon , N. , Doucet , A. and Freitas , J. D . 2001 . Sequential Monte Carlo Methods in Practice . Springer-Verlag, New York , NY .
  26. Gordon , N. , Salmond , D. and Smith , A . 1993 . Novel approach to non-linear/non-gaussian bayesian state estimation . IEEE Process.-F 140 ( 2 ).
  27. Hamill , T. and Snyder , C . 2000 . A hybrid ensemble Kalman filter-3d variational analysis scheme . Mon. Wea. Rev . 128 ( 8 ), 2905 2919 .
  28. Harlim , J. and Hunt , B . 2007a . Four-dimensional local ensemble transform Kalman filter: numerical experiments with a global circulation model . Tellus 59A ( 5 ), 731 748 .
  29. Harlim , J. and Hunt , B . 2007b . A non-gaussian ensemble filter for assimilating infrequent noisy observations . Tellus 59A , 225 237 .
  30. Harlim , J. and Majda , A . 2009 . Catastrophic filter divergence in filtering nonlinear dissipative systems. Commun . Math. Sc i . 7 ( 3 ).
  31. Houtekamer , P. and Mitchell , H . 2001 . A sequential ensemble Kalman filter for atmospheric data assimilation . Mon. Wea. Rev . 129 ( 1 ), 123 137 .
  32. Houtekamer , P. L. and Mitchell , H . 1998 . Data assimilation using an ensemble Kalman filter technique . Mon. Wea. Rev . 126 ( 3 ), 796 811 .
  33. Houtekamer , P. L. and Mitchell , H . 2006 . Ensemble Kalman filtering . Quart. J. Roy. Meteorol. Soc . 131 ( 613 ), 3269 3289 .
  34. Houtekamer , P. L. , Mitchell , H. , Pellerin , G. , Buehner , M. , Charron , M. and co-authors . 2005 . Atmospheric data assimilation with an ensemble Kalman filter: results with real observations. Mon. Wea. Rev . 133 ( 3 ), 604 620 .
  35. Hunt , B. , Kalnay , E. , Kostelich , E. , Ott , E. , Patil , D. and co-authors . 2004 . Four-dimensional ensemble Kalman filtering. Tellus 56A , 273 277 .
  36. Hunt , B. , Kostelich , E. and Szunyogh , J. 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230 , 112 126 .
  37. Julier , S. and Uhlmann , J . 1997 . A new extension of the Kalman filter to nonlinear systems . In:Proceedings of the International Symposium Aerospace/Defense Sensing, Simul. Controls , 182 193 .
  38. Kalman , R . 1960 . A new approach to linear filtering and prediction problems . Trans. ASME - J. Basic Eng . 82 , 35 45 .
  39. Kalman , R. and Bucy , R . 1961 . New results in linear filtering and prediction theory . Trans. ASME - J. Basic Eng . 83 , 95 107 .
  40. Kivman , G. A . 2003 . Sequential parameter estimation for stochastic systems . Nonlin. Process. Geophys . 10 , 253 259 .
  41. Kong , A. , Liu , J. and Wong , W . 1994 . Sequential imputations and Bayesian missing data problems . J. Am. Stat. Assoc . 89 ( 425 ), 278 288 .
  42. Kuhl , D. , Szunyogh , I. , Kostelich , E. J. , Patil , D. J. , Gyarmati , G. and co-authors . 2007 . Assessing predictability with a local ensemble Kalman filter. J. Atmos. Sci . 64 , 1116 1140 .
  43. Le Dimet , F.-X. and Talagrand , O . 1986 . Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects . Tellus 38A , 97 110 .
  44. Le Gland , F. , Monbet , V. and Tran , V . 2009 . Large sample asymptotics for the ensemble Kalman filter . Technical Report 7014 ,
  45. Le Gland , F. , Monbet , V. and Tran , V . 2010 . Large sample asymptotics for the ensemble Kalman filter. In: Handbook on Nonlinear Filtering , ( eds D. Crisan and B. Rozovskii ), Oxford University Press , in press .
  46. Liu , J. , Fertig , E. , Li , H. , Kalnay , E. , Hunt , B. and co-authors . 2008 . Comparison between local ensemble transform Kalman filter and PSAS in the nasa finite volume gcm-perfect model experiments. Nonlin. Process. Geophys . 15 ( 4 ), 645 659 .
  47. Lorene , A . 1981 . A global three-dimensional multivariate statistical interpolation scheme . Mon. Wea. Rev . 109 ( 4 ), 701 721 .
  48. Miyoshi , T. and Yamane , S . 2007 . Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution . Mon. Wea. Rev . 135 ( 11 ), 3841 3861 .
  49. Musso , C. , Oudjane , N. and Le Gland , F . 2001 . Improving regularized particle filters. In: Sequential Monte Carlo Methods in Practice , (eds A. Doucet , N. de Freitas and N. Gordon ), Statistics for Engineering and Information Science. Springer-Verlag , New York , 247 271 .
  50. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V. , Kostelich , E. J. and co-authors . 2004 . A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A , 415 428 .
  51. Patil , D. , Hunt , B. and Carton , J . 2001 . Identifying low-dimensional nonlinear behavior in atmospheric data . Mon. Wea. Rev . 129 ( 8 ), 2116 2125 .
  52. Pham , D. T . 2001 . Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon . Wea. Re v . 129 , 1194 1207 .
  53. Rémy , S. , Pannekoucke , O. , Bergot , T. and Baehr , C . 2010 . Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting . Quart. J. Roy. Meteorol. Soc . , submitted .
  54. Shu , C.-W . 1998 . Advanced Numerical Approximation of Nonlinear Hyperbolic Equations , Volume 1697 of Lecture Notes in Mathematics. Springer , Berlin/Heidelberg , 325 432 .
  55. Snyder , C. , Bengtsson , T. , Bickel , P. and Anderson , J . 2008 . Obstacles to high-dimensional particle filtering . Mon. Wea. Rev . 136 ( 12 ), 4629 4640 .
  56. Szunyogh , I. , Kostelich , E. J. , Gyarmati , G. , Kalnay , E. , Hunt , B. and co-authors . 2008 . A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus 60A , 113 130 .
  57. Tippett , M. , Anderson , J. , Craig , C. B. , Hamill , T. and Whitaker , J . 2003 . Ensemble square root filters . Mon. Wea. Rev . 131 ( 7 ), 1485 1490 .
  58. van der Merwe , R. , de Freitas , N. , Doucet , A. and Wan , E . 2001 . The unscented particle filter . Adv. Neural Inform. Process. Syst . , 13 , 584 590 .
  59. van Leeuwen , P. J . 2002 . Ensemble Kalman filters, sequential importance resampling and beyond. In ECMWF Workshop on the Role of the Upper Ocean in the Medium and Extended Range Forecasting, ECMWF, Reading, UK , 46 56 .
  60. van Leeuwen , P. J . 2003 . A variance-minimizing filter for large-scale applications . Mon. Wea. Rev . 131 ( 9 ), 2071 2084 .
  61. van Leeuwen , P. J . 2009 . Particle filtering in geophysical systems . Mon. Wea. Rev . , 137 ( 2 ), 4089 4114 .
  62. Whitaker , J. and Hamill , T . 2002 . Ensemble data assimilation without perturbed observations . Mon. Wea. Rev . 130 ( 7 ), 1913 1924 .
  63. Whitaker , J. , Hamill , T. , Song , X. W. Y. and Toth , Z . 2008 . Ensemble data assimilation with the NCEP global forecast system . Mon. Wea. Rev . 136 ( 2 ), 463 482 .
  64. Xiong , X. , Navon , I. and Uzunoglu , B . 2006 . A note on the particle filter with posterior gaussian resampling . Tellus 58A , 456 460 .
  65. Zhou , Y. , McLaughlin , D. and Entelchabi , D . 2006 . Assessing the performance of the ensemble Kalman filter for land surface data assimilation . Mon. Wea. Rev . 134 ( 8 ), 2128 2142 .
  66. Zupanski , M . 2005 . Maximum likelihood ensemble filter: theoretical aspects . Mon. Wea. Rev . 133 ( 6 ), 1710 1726 .
Language: English
Page range: 673 - 697
Submitted on: Oct 23, 2009
Accepted on: Apr 13, 2010
Published on: Jan 1, 2010
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2010 Nicolas Papadakis, Etienne Mémin, Anne Cuzol, Nicolas Gengembre, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.