• \
    Start Submission Become a Reviewer

    Reading: Data assimilation for re-analyses: potential gains from full use of post-analysis-time obser...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Data assimilation for re-analyses: potential gains from full use of post-analysis-time observations

    Authors:

    Martin Juckes ,

    British Atmospheric Data Centre, Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, OX11 0QX, GB
    X close

    Bryan Lawrence

    British Atmospheric Data Centre, Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, OX11 0QX, GB
    X close

    Abstract

    In recent years a number of operational meteorological centres have completed multidecadal reanalyses of their observation records using a version of their operational analysis systems. These operational systems aim to approximate the best possible analysis of the atmospheric state at a given time using all observations made prior to that time, and require major resources to produce. Re-analyses are made with the same real-time systems because they can be done as marginal activities on the back of operational efforts. In this paper, we examine some of the salient differences between the use of optimal real-time analyses and optimal retrospective analyses in the context of a simple linear system. In this case, the optimal real-time analysis could be obtained by the Kalman filter. When observations are available both before and after the analysis time the additional information can, in principle, be exploited to improve on the Kalman filter analysis. For linear systems the optimal retrospective analysis is given by the Kalman smoother, which combines a forward and backward Kalman filter. Results comparing these methods are presented which demonstrate the importance of using all the available data for a retrospective analysis. While using all future data is not yet tractable for retrospective meteorological analyses, such techniques are of use for more limited re-analysis.

    How to Cite: Juckes, M. and Lawrence, B., 2006. Data assimilation for re-analyses: potential gains from full use of post-analysis-time observations. Tellus A: Dynamic Meteorology and Oceanography, 58(2), pp.171–178. DOI: http://doi.org/10.1111/j.1600-0870.2006.00167.x
      Published on 01 Jan 2006
    Peer Reviewed
     CC BY 4.0
     Accepted on 30 Sep 2005            Submitted on 14 Mar 2005

    References

    1. Bell , M. J. , Martin , M. J. and Nichols , N. K. 2004 . Assimilation of data into an ocean model with systematic errors near the equator . Q. J. R. Meterol. Soc . 130 , 873 – 893 .  

    2. Bennett , A. E 1992 . Inverse methods in physical oceanography . Cambridge University Press , New York, NY , 347 pp .  

    3. Cohn , S. E. , Sivakumaran , N. S. and Todling , R. 1994 . A fixed-lag Kalman smoother for retrospective data assimilation . Mon. Weather Rev . 122 , 2838 – 2867 .  

    4. Dee , D. P. and Da Silva , A. M. 1998 . Data assimilation in the presence of forecast bias . Q. J. R. Meterol. Soc . 124 , 269 – 295 .  

    5. Dee , D. P. and Todling , R. 2000 . Data assimilation in the presence of forecast bias: the GEOS moisture analysis . Mon. Weather Rev . 128 , 3268 – 3282 .  

    6. Griffith , A. K. and Nichols , N. K. 1996 . Accounting for model error in data assimilation using adjoint methods. In: Computational Differen-tiation: Techniques, Applications and Tools (eds. M. Berz , C. Bischof , G. Corliss , and A. Greiwank ). SIAM, Philadelphia , 195 – 204 .  

    7. Juckes , M. N. 2005 . The direct inversion method for data assimilation us-ing isentropic tracer advection . Atmos. Chem. Phys. Discuss . 5 , 8879 – 8923 .  

    8. Kalman , R. E. and Bucy , R. 1961 . New results in linear filtering and prediction . J. Basic Eng . 83D , 95 – 108 .  

    9. Kalnay , E. , Kanamitsu , M. , Kistler , R. , Collins , W. , Deaven , D. and co-authors . 1996 . The NCEP/NCAR Reanalysis Project. Bull. Am. Meteorol. Soc . 77 , 437 – 471 .  

    10. Kloeden , P. E. and Platen , E. 1992 . Numerical solution of stochastic differential equations . Springer , Berlin , 292 pp .  

    11. Li , Z. and Navon , I. M. 2001 . Optimality of variational data assimilation and its relationship with the Kalman filter and smoother . Q. J. R. Meterol. Soc . 127 , 661 – 684 .  

    12. Rogers , C. D. 2000 . Inverse methods for atmospheric sounding . World Scientific Publishing , London , 238 pp .  

    13. Simmons , A. J. and Gibson , J. K. 2000 . ERA-40 Project Report Series No. 1, The ERA-40 Project Plan, ECMWF, Reading, UK .  

    14. Tremolet , Y. 2004 . Diagnostics of linear and incremental approximations in 4D-Var . Q. J. R. Meterol. Soc . 130 , 2233 – 2251 .  

    Jump to Discussions Related content
    comments powered by Disqus