• \
    Start Submission Become a Reviewer

    Reading: Local ensemble Kalman filtering in the presence of model bias

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Local ensemble Kalman filtering in the presence of model bias

    Authors:

    Seung-Jong Baek ,

    Institute for Research in Electronics and Applied Physics, and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, US
    X close

    Brian R. Hunt,

    Institute for Physical Science and Technology, and Department of Mathematics, University of Maryland, College Park, MD, US
    X close

    Eugenia Kalnay,

    Institute for Physical Science and Technology, and Department of Atmospheric and Oceanic Science, University of Maryland,College Park, MD, US
    X close

    Edward Ott,

    Institute for Research in Electronics and Applied Physics, and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD; Department of Physics, University of Maryland, College Park, MD, US
    X close

    Istvan Szunyogh

    Institute for Physical Science and Technology, and Department of Atmospheric and Oceanic Science, University of Maryland,College Park, MD, US
    X close

    Abstract

    We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias.We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter through numerical experiments incorporating various model biases into the model of Lorenz and Emanuel. Our results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the forecast model. In particular, we find that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter.

    How to Cite: Baek, S.-J., Hunt, B.R., Kalnay, E., Ott, E. and Szunyogh, I., 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus A: Dynamic Meteorology and Oceanography, 58(3), pp.293–306. DOI: http://doi.org/10.1111/j.1600-0870.2006.00178.x
      Published on 01 Jan 2006
    Peer Reviewed
     CC BY 4.0
     Accepted on 5 Dec 2005            Submitted on 6 Apr 2005

    References

    1. Anderson , J. L. 2001 . An ensemble adjustment Kalman filter for data assimilation . Mon. Wea. Rev . 129 , 2884 – 2903 .  

    2. Annan , J. D. and Hargreaves , J. C. 2004 . Efficient parameter estimation for a highly chaotic system . Tellus 56A , 520 – 526 .  

    3. Baer , E and Tribbia , J. J. 1977 . On complete filtering of gravity modes through nonlinear initialization. Mon. Wea. Rev . 105 , 1536 – 1539 .  

    4. Bell , M. J. , Martin , M. J. and Nichols , N. K. 2004 . Assimilation of data into an ocean model with systematic errors near the equator . Quart. J. Roy. Meteor. Soc . 130 , 873 – 893 .  

    5. Bishop , C. H. , Etherton , B. J. and Majumdar , S. J. 2001 . Adaptive sam-pling with the ensemble transform Kalman filter. Part I: Theoretical aspects . Mon. Wea. Rev . 129 , 420 – 436 .  

    6. Carton , J. A. , Chepurin , G. and Cao , X. 2000 . A simple ocean data assimilation analysis of the global upper ocean 1950-1995. Part I: Methodology . J. Phys. Oceanogr . 30 , 294 – 309 .  

    7. Cohn , S. E. 1997 . An introduction to estimation theory . J. Meteor. Soc. Japan 75 , 257 – 288 .  

    8. Daley , R. 1992 . The effect of serially correlated observation and model error on atmospheric data assimilation . Mon. Wea. Rev . 120 , 164 – 177 .  

    9. Dee , D. P. and Da Silva , A. M. 1998 . Data assimilation in the presence of forecast bias . Quart. J. Roy. Meteor. Soc . 124 , 269 – 295 .  

    10. Dee , D. P. and Todling , R. 2000 . Data assimilation in the presence of fore-cast bias: The GEOS moisture analysis . Mon. Wea. Rev . 128 , 3268 – 3282 .  

    11. Derber , J. C. 1989 . A variational continuous assimilation technique . Mon. Wea. Rev . 117 , 2437 – 2446 .  

    12. Etherton , B. J. and Bishop , C. H. 2004 . Resilience of hybrid ensem-ble/3DVAR analysis schemes to model error and ensemble covariance error . Mon. Wea. Rev . 132 , 1065 – 1080 .  

    13. Evensen , G. 1994 . Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics . J. Geophys. Res . 99C5 , 10 143 – 10 162 .  

    14. Friedland , B. 1969 . Treatment of bias in recursive filtering . IEEE Trans. Autom. Contr . 14 , 359 – 367 .  

    15. Ghil , M. , Cohn , S. , Tavantzis J. , Bube , K. and Isaacson , E. 1981 . Ap-plications of estimation theory to numerical weather prediction. In: Dynamic Meteorology: Data Assimilation Methods (eds. A. L. Bengts-son , M. Ghil , and E. Kallen ) Springer-Verlag, New York , 139 – 224 .  

    16. Hamill , T. M. and Snyder , C. 2000 . A hybrid ensemble kalman filter-3D variational analysis scheme . Men. Wea. Rev . 128 , 2905 – 2919 .  

    17. Hamill , T. M. , Whitaker , J. S. and Snyder , C. 2001 . Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter . Mon. Wea. Rev . 129 , 2776 – 2790 .  

    18. Hamill , T. M. and Whitaker , J. S. 2005 . Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches . Mon. Wea. Rev . 133 , 3132 – 3147 .  

    19. Hansen , J. A. 2002. Accounting for model error in ensemble-based state estimation and forecasting. Mon. Wea. Rev . 130 , 2373 – 2391 .  

    20. Houtekamer , P. L. and Mitchell , H. L. 1998 . Data assimilation using an ensemble kalman filter technique . Mon. Wea. Rev . 126 , 796 – 811 .  

    21. Houtekamer , P. L. and Mitchell , H. L. 2001 . A sequential ensemble kalman filter for atmospheric data assimilation . Mon. Wea. Rev . 129 , 123 – 137 .  

    22. Houtekamer , P. L. , Mitchell , H. L. , Pellerin , G. , Buehner , M. and Charron , M. , 2005. Atmospheric data assimilation with an ensem-ble kalman filter: Results with real observations. Mon. Wea. Rev . 133 , 604 – 620 .  

    23. Jazwinski , A. H. 1970 . Stochastic Processes and Filtering Theory . Aca-demic Press , San Diego .  

    24. Lorenz , E. N. and Emanuel , K. A. 1998 . Optimal sites for supplementary weather observations: Simulation with a small model . J. Atmos. Sci . 55 , 399 – 414 .  

    25. Lynch , P. and Huang , X.-Y. 1992 . Initialization of the HIRLAM model using a digital filter . Mon. Wea. Rev . 120 , 1019 – 1034 .  

    26. Lynch , P. 1997 . The Dolph-Chebyshev window: A simple optimal filter . Mon. Wea. Rev . 125 , 655 – 660 .  

    27. Machenhauer , B. 1977 . On the dynamics of gravity oscillations in a shallow water model with applications to normal mode initialization . Contrib. Atmos. Phys . 50 , 253 – 271 .  

    28. Margolin , L. G. , Titi , E. S. and Wynne , S. 2003 . The postprocess-ing Galerkin and nonlinear Galerkin methods-A truncation analysis point of view . SIAM J. Numer. Anal . 41 , 695 – 714 .  

    29. Martin , M. J. , Bell , M. J. and Nichols , N. K. 2002 . Estimation of system-atic error in an equatorial ocean model using data assimilation. Int. J. Numer. Meth. Fluids 40 , 435 – 444 .  

    30. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V. and Kostelich , E. J. 2004a. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A , 415 – 428 .  

    31. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V. and Kostelich , E. J. 2004b. Estimating the state of large spatio-temporally chaotic systems. Phys. Lett. A 330 , 365 – 370 .  

    32. Szunyogh , I. , Kostelich , E. J. , Gyarmati , G. , Patil , D. J. and Hunt , B. R. 2005 . Assessing a local ensemble Kalman filter: Perfect model experiments with the NCEP global model. Tellus 57A , 528 – 545 .  

    33. Whitaker , J. S. and Hamill , T. M. 2002 . Ensemble data assimilation without perturbed observations. Mon. Wea. Rev . 130 , 1913 – 1924 .  

    34. Whitaker , J. S. , Compo , G. P. , Wei , X. and Hamil T. M. 2004 . Reanalysis without radiosondes using ensemble data assimilation . Mon. Wea. Rev . 132 , 1190 – 1200 .  

    35. Zupanski , D. 1997 . A general weak constraint applicable to operational 4DVAR data assimilation systems . Mon. Wea. Rev . 125 , 2274 – 2292 .  

    Jump to Discussions Related content
    comments powered by Disqus