Skip to main content
Have a personal or library account? Click to login
A POD-based ensemble four-dimensional variational assimilation method Cover

A POD-based ensemble four-dimensional variational assimilation method

By: ,   and    
Open Access
|Jan 2011

References

  1. Bauer , R , Lopez , R , Benedetti , A. , Salmond , D. , Saarinen , S. and co-authors . 2006. Implementation of 1D+4D-Var assimilation of precipitation affected microwave radiances at ECMWF 4D-Var. Q. J. R. MeteoroL Soc . 132 , 2307-233 2 .
  2. Bormann , N. and Thepaut , J. N . 2004 . Impact of MODIS polar winds in ECMWF’s 4DVAR data assimilation system. Mon . Wea. Re v . 132 , 929 940 .
  3. Caya , A. , Sun , J. and Snyder , C . 2005 . A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon . Wea. Re v . 133 , 3081 3094 .
  4. Cheng , H. , Jardalc , M. , Alexe , M. and Sandu , A . 2010 . A hybrid approach to estimating error covariances in variational data assimilation . Taus 62A , 288 297 .
  5. Courtier , P. and Talagrand , O. 1987. Variational assimilation of meteoro-logical observations with the adjoint vorticity equation IL numerical results. Q. J. R. MeteoroL Soc . 113 , 1329-134 7 .
  6. Courtier , P. , Thepaut , J. N. and Hollingsworth , A . 1994 . A strategy for operational implementation of 4DVar using an incremental approach . Q. J. R. MeteoroL Soc . 120 , 1367 1387 .
  7. Evensen , G . 1994 . Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statis-tics . J. Geophys. Res . 99 ( C5 ), 10143 10162 .
  8. Evensen , G . 2004 . Sampling strategies and square root analysis schemes for the EnKF . Ocean Dyn . 54 , 539 560 , 10.1007/s10236-004-0099-2 .
  9. Evensen , G. and van Leeuwen , P. J . 2000 . An ensemble Kalman smoother for nonlinear dynamics. Mon . Wea. Re v . 128 , 1852 1867 .
  10. Fertig , E. , Harlim , J. and Hunt , B . 2007 . A comparative study of 4DVar and 4D ensemble Kalman filter: perfect model simulations with Lorenz-96 . Tellus 59 , 96 101 .
  11. Gauthier , R , Tanguay , M. , Laroche , S. , Pellerin , S. and Morneau , J . 2007 . Extension of 3DVAR to 4DVAR: implementation of 4DVAR at the Meteorological Service of Canada . Mon. Wea. Rev . 135 ( 6 ), 2339 2354 .
  12. Hamill , T. M. and Snyder , C . 2000 . A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon . Wea. Re v . 128 , 2905 2919 .
  13. Houtelcamer , P. L. and Mitchell , H. L . 1998 . Data assimilation using an ensemble Kalman filter technique. Mon . Wea. Re v . 126 , 796 811 .
  14. Houtekamer , P. L. and Mitchell , H. L . 2001 . A sequential ensemble Kalman filter for atmospheric data assimilation. Mon . Wea. Re v . 129 , 123 137 .
  15. Hunt , B. R. , Kalnay E. , Kostelich E. J. , Ott E. , Patil D. J. and co-authors 2004. Four-dimensional ensemble Kalman filtering. Tellus 56A , 273 - 277 .
  16. Le Dimet , F. X. and Talagrand , O. 1986. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus 38A , 97 - 110 .
  17. Lewis , J. M. and Derber , J. C . 1985 . The use of the adjoint equation to solve a variational adjustment problem with advective constraints . Tellus 37A , 309 322 .
  18. Liu , D. C. and Nocedal J . 1989 . On the limited memory BFGS method for large scale optimization . Math. Pmg . 45 , 503 528 .
  19. Lorene , A . 2003 . The potential of the Ensemble Kalman Filter for NVVP: a comparison with 4DVar . Q. J. R. MeteoroL Soc . 129 , 3183 3203 .
  20. Lorenz , E . 1996 . Predictability: a problem partly solved. In: Proc. Semi-nar on Predictability . Volume 1, reading, ECMWF, United Kingdom, 1 - 19 .
  21. Park , K. and Zou , X . 2004 . Toward developing an objective 4DVAR BDA scheme for hurricane initialization based on TPC observed pa-rameters . Mon. Wea. Rev . 132 ( 8 ), 2054 2069 .
  22. Rosmond , T. and Xu , L . 2006 . Development of NAVDAS-AR: non-linear formulation and outer loop tests . Tellus A 58 ( 1 ), 45 58 .
  23. Tian , X. , Xie , Z. and Dai , A . 2008 . An ensemble-based explicit four-dimensional variational assimilation method . J. Geophys. Res . 113 , D21124 , 10.1029/2008JD010358 .
  24. Vermeulen , P. T. M. and Heeminlc , A.W . 2006 . Model-reduced varia-tional data assimilation . Mon. Wea. Rev . 134 ( 10 ), 2888 2899 .
  25. Wang , B. , Liu , J. , Wang , S. , Cheng , W. , Liu , J. and co-authors . 2010 . An economical approach to four-dimensional variational data assim-ilation. Adv. Atmos. Sci . 27 ( 4 ), 715-727, 10.1007/s00376-009-9122-3 .
  26. Zhang , F. , Snyder , C. and Sun , J . 2004 . Tests of an ensemble Kalman filter for convective-scale data assimilation: impact of initial estimate and observations. Mon . Wea. Re v . 132 , 1238 1253 .
  27. Zhang , F. Q. , Zhang , M. and Hansen , J. A . 2009 . Coupling ensemble Kalman filter with four dimensional variational data assimilation . Adv. Atmos. Sci . 26 ( 1 ), 1 8 , 10.1007/s00376-009-0001-8 .
  28. Zupanski , M . 2005 . Maximum likelihood ensemble filter: theoretical aspects. Mon . Wea. Re v . 133 , 1710 1726 .
Language: English
Page range: 805 - 816
Submitted on: Nov 9, 2010
Accepted on: Apr 18, 2011
Published on: Jan 1, 2011
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2011 Xiangjun Tian, Zhenghui Xie, Qin Sun, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.