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Original Research Papers

Data assimilation in a system with two scales—combining two initialization techniques

Authors:

Joaquim Ballabrera-Poy ,

Institut de Ciències del Mar, CSIC, Passeig Marítim de la Barceloneta, 37-49, 08003 Barcelona, ES
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Eugenia Kalnay,

AOSC, University of Maryland, College Park, MD, US
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Shu-Chih Yang

AOSC, University of Maryland, College Park, MD; Global Modeling and Assimilation Office (GMAO), NASA/GSFC, Greenbelt, MD, US
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Abstract

An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observations.

How to Cite: Ballabrera-Poy, J., Kalnay, E. and Yang, S.-C., 2009. Data assimilation in a system with two scales—combining two initialization techniques. Tellus A: Dynamic Meteorology and Oceanography, 61(4), pp.539–549. DOI: http://doi.org/10.1111/j.1600-0870.2009.00400.x
  Published on 01 Jan 2009
 Accepted on 5 Mar 2009            Submitted on 14 Apr 2008

References

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

  2. Anthes , R. A. 1974 . Data Assimilation and initialization of hurricane prediction models. J. Atmos. Sci . 31 , 702 - 719 .  

  3. Ballabrera-Poy , J. , Brasseur , P. and Verron , J. 2001 . Dynamical evolution of the error statistics with the SEEK filter to assimilate altimetric data in eddy-resolving ocean models . Q. J. R. MeteoroL Soc . 127 , 233 – 253 .  

  4. Cane , M. A. , Kaplan , A. , Miller , R. N. , Tang , B. , Hackert , E. C. and co-authors. 1996 . Mapping tropical Pacific sea level: data assimilation via a reduced state space Kalman filter. J. Geophys. Res . 101 , 22599 - 22617 .  

  5. Corazza , M. , Kalnay , E. and Yang , S.-C. 2007 . An implementation of the Local Ensemble Kalman Filter in a quasi geostrophic model and comparison with 3D-Var . Nonlin. Proc. Geophys . 14 , 89 – 101 .  

  6. Deschamps , L. and Talagrand , J. 2007. On some aspects of the definition of initial conditions for ensemble prediction. Mon. Wea. Rev . 135 , 3260 - 3272 .  

  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 , 10143 – 10162 .  

  8. Hunt , B. R. , Kostelich , E. J. and Szunyogh , I . 2007 . Efficient data assim-ilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230 , 112 - 126 .  

  9. Kalman , R. E. and Bucy , R. S. 1960 . A new approach to linear filtering and prediction problems . J. Basic Eng. (ASME) 82D , 35 – 45 .  

  10. Kalnay , E. 2003 . Atmospheric Modeling, Data Assimilation and Predictability . Cambridge University Press , Cambridge , 364 pp .  

  11. Kalnay , E. , Li , H. , Miyoshi , T. , Yang , S.-C. and Ballabrera-Poy , J. 2007. 4-D-Var or ensemble Kalman filter? Tellus 59A , 758 - 773 .  

  12. Lorene , A.C. 2003 . The potential of the ensemble Kalman filter for NWP—a comparison with 4D-Var . Q. J. R. Meteorol. Soc . 129 , 3183 – 3203 .  

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

  14. Meehl , G. A. , Lukas , R. , Kiladis , G. N., Weickmann , K. M. , Matthews , A. J. and co-authors. 2001 . A conceptual framework for time and space scale interactions in the climate system. Clim. Dyn . 17 , 753 - 775 .  

  15. Miller , R.N. and Cane , M. A 1989 . A Kalman Filter analysis of sea level height in the tropical Pacific . J. Phys. Oceanogr 19 , 773 – 790 .  

  16. 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 .  

  17. Saha , S. , Nadiga , S. , Thiaw , C. , Wang , J. , Wang , W. and co-authors . 2006. The NCEP Climate Forecast System. J. Clim . 19 , 3483 - 3517 .  

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

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