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Original Research Papers

A local ensemble transform Kalman filter data assimilation system for the NCEP global model

Authors:

Istvan Szunyogh ,

Department of Atmospheric and Oceanic Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD, US
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Eric J. Kostelich,

Department of Mathematics and Statistics, Arizona State University, AZ, US
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Gyorgyi Gyarmati,

Institute for Physical Science and Technology, University of Maryland, College Park, MD, US
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Eugenia Kalnay,

Department of Atmospheric and Oceanic Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD, US
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Brian R. Hunt,

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

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

Department of Atmospheric and Oceanic Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD, US
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James A. Yorke

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

The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated.

Numerical experiments are carried out at model resolution T62L28. All atmospheric observations that were operationally assimilated by NCEP in 2004, except for satellite radiances, are assimilated with the LETKF. The accuracy of the LETKF analyses is evaluated by comparing it to that of the Spectral Statistical Interpolation (SSI), which was the operational global data assimilation scheme of NCEP in 2004. For the selected set of observations, the LETKF analyses are more accurate than the SSI analyses in the Southern Hemisphere extratropics and are comparably accurate in the Northern Hemisphere extratropics and in the Tropics.

The computational wall-clock times achieved on a Beowulf cluster of 3.6 GHz Xeon processors make our implementation of the LETKF on the NCEP GFS a widely applicable analysis-forecast system, especially for research purposes. For instance, the generation of four daily analyses at the resolution of the NCAR-NCEP reanalysis (T62L28) for a full season (90 d), using 40 processors, takes less than 4 d of wall-clock time.

How to Cite: Szunyogh, I., Kostelich, E.J., Gyarmati, G., Kalnay, E., Hunt, B.R., Ott, E., Satterfield, E. and Yorke, J.A., 2008. A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus A: Dynamic Meteorology and Oceanography, 60(1), pp.113–130. DOI: http://doi.org/10.1111/j.1600-0870.2007.00274.x
  Published on 01 Jan 2008
 Accepted on 21 Aug 2007            Submitted on 14 Dec 2006

References

  1. Anderson , E. , Bai , Z. , Bischof , C. , Blackford , S. , Demmel , J. and co-authors 1999 . LAPACK Users’ Guide, third ed. Society for Industrial and Applied Mathematics, Philadelphia. An online version is available at www.netlib.org/lapack/lug.  

  2. 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 58A , 293 – 306 .  

  3. Bishop , C. H. , Etherton , B. J. and Majumdar , S. 2001 . Adaptive sampling with the Ensemble Transform Kalman Filter. Part I: theoretical aspects. Mon. Wea. Re v . 129 , 420 – 436 .  

  4. Evensen , G. 2007 . Data Assimilation . The Ensemble Kalman Filter. Springer , Berlin .  

  5. Evensen , G. 2003 . The ensemble Kalman filter: theoretical formulation and practical implementation . Ocean Dyn . 53 , 343 – 367 .  

  6. Fertig , E. J. , Hunt , B. R. , Ott , E. and Szunyogh , I., 2007 . Assimilating nonlocal observations with a local ensemble Kalman filter. Tellus 59A in press .  

  7. Fertig , E. J. , Harlim , J. and Hunt , B. R. 2006 . A comparative study of 4D-Var and 4D ensemble Kalman filter: perfect model simulations with Lorenz-96 . Tellus 59A , 96 – 100 .  

  8. Global Climate and Weather Modeling Branch, Environmental Modeling Center 2003 . The GFS atmospheric model. Office Note 442, http://www.emc.ncep.noaa.gov/officenotes/FullTOC.html. NCEP, NOAA/NWS, 5200 Auth Road, Camp Springs, MD 20742.  

  9. Gonnet , G. H. and Baeza-Yates , R. A. 1990 . Handbook of Algorithms and Data Structures Chapter 6, 2nd Edition. Addison-Wesley, Reading, MA. 124 - 156 .  

  10. Hamill , T. M. 2006 . Ensemble-based data assimilation . In: Predictability of Weather and Climate (eds T . Palmer and R. Hagedorn Cambridge University Press , Cambridge .  

  11. Hamill , T. M. , Snyder , C., 2000 . A hybrid ensemble Kalman Filter-3D Variational Analysis Scheme. Mon. Wea. Re v . 128 , 2905 – 2919 .  

  12. Houtekamer , P. L. and Mitchell , H. L. 2005 . Ensemble Kalman filtering . Quart. J. Roy. Meteor Soc . 131 , 3269 – 3289 .  

  13. Houtekamer , P. L. , Mitchell , H. L. , Pellerin , G. , Buehner , M. , Charron , M. and co-authors 2005 . Atmospheric data assimilation with the en-semble Kalman filter: Results with real observations. Mon. Wea. Rev . 133 , 604 - 620 .  

  14. Hunt , B. R. , Kostelich , E. J. and Szunyogh , J. 2007. Efficient data assimi-lation for spatiotemporal chaos: a Local Ensemble Transform Kalman Filter. Physica D, 230 , 112 - 126 .  

  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. Kuhl , D. , Szunyogh , I. , Kostelich , E. J. , Gyarmati , G. , Patil , D. J. and co-authors 2005 . Assessing predictability with a local ensemble Kalman filter . J. Atmos. Sci . 64 , 1116 - 1140 .  

  17. Lynch , P. and Huang , P. M. 1992 . Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev . 120 , 1019 - 1034 .  

  18. Oczkowslci , M. , Szunyogh , I. and Patil , D. J. 2005 . Mechanisms for the development of locally low dimensional atmospheric dynamics . J. Atmos. Sci . 62 , 1135 – 1156 .  

  19. Ott , E. , Hunt , B. H., 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 .  

  20. Parrish , D. and Derber , J. 1992 . The National Meteorological Center’s spectral statistical interpolation analysis system . Mon. Wea. Rev . 120 , 1747 – 1763 .  

  21. Szunyogh , I. , Kostelich , E. J. , Gyarmati , G. , Patil , D. J. , Hunt , B. R. and co-authors. 2005 . Assessing a local ensemble Kalman filter: perfect model experiments with the National Center for Environmental Prediction global model . Tellus 57A , 528 – 545  

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

  23. Whitaker , J. S. , Hamill , T. M. , Wei , X. , Song , Y. and Toth , Z. 2006 . Ensemble data assimilation with the NCEP Global Forecast System . Mon. Wea. Rev ., in press .  

  24. Whitaker , J. S. , Compo , G. P. , Wei , X. and Hamill , T. H. 2004 . Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev . 132 , 1190 - 1200 .  

  25. Wilks , D. S., 2006 . Statistical Methods in the Atmospheric Sciences , 2nd Edition. Academic Press , Burlington , MA , 143 – 146 .  

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