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Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, G.; Patil, D. J.; Hunt, Brian R.; Kalnay, Eugenia; Ott, Edward; Yorke, James A. (2005)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. The performance of the data assimilation system is assessed for different configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs. The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles. Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.
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    • Anderson, J. L. 2001. An ensemble adjustment filter for data assimilation. Mon. Wea. Rev. 129, 2884-2903.
    • Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the non-linear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev. 127, 2741-2758.
    • Anderson, E., Bai, Z., Bischof, C., Blackford, L. S., Demmel, J. and co-authors. 1999. LAPACK Users' Guide, 3rd Edition. Society for Industrial and Applied Mathematics, Philadelphia, PA.
    • Arakawa, A. and Schubert, W. H. 1974. Interaction of a cumulus cloud ensemble with the large-scale environment.Part I. J. Atmos. Sci. 31, 674-701.
    • Baek, S-J., Hunt, B. R., Szunyogh, I., Zimin. A. and Ott, E. 2004. Localized error bursts in estimating the state of spatiotemporal chaos. Chaos 14, 1042-1049.
    • Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev. 129, 420-436.
    • Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M. and Blade, I. 1999. The effective number of spatial degrees of freedom of a time-varying field. J. Climate 12, 1990-2009.
    • Daley, R. 1991. Atmospheric Data Analysis. Cambridge Univ. Press, Cambridge.
    • Dee, D. P. 1995. Testing the perfect-model assumption in variational data assimilation. Proc. 2nd Int. Symp. on Assimilation of Observations in Meteorology and Oceanography. World Meteorological Organization, Tokyo, Japan, 225-228.
    • Dongarra, J. J., Du Croz, J., Hammarling, S. and Hanson, R. J. 1988. An extended set of FORTRAN Basic Linear Algebra Subprograms. ACM Trans. Math. Soft. 14, 1-17.
    • Dongarra, J. J., Du Croz, J., Hammarling, S. and Duff, I. S. 1990. A set of Level 3 Basic Linear Algebra Subprograms. ACM Trans. Math. Soft. 16, 1-17.
    • Dowell, D. C., Zhang, F., Wicker, L. J., Snyder, C. and Crook, N. A. 2004. Wind and thermodynamic retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: ensemble Kalman filter experiments. Mon. Wea. Rev. 132, 1982-2005.
    • Emanuel, K. A. 1994. Atmospheric Convection. Oxford Univ. Press, New York.
    • Hamill, T. M., Whitaker, J. and Snyder, C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman Filter. Mon. Wea. Rev. 129, 2776-2790.
    • Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman Filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123-137.
    • Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M. and co-authors. 2005. Atmospheric data assimilation with the ensemble Kalman filter: results with real observations. Mon. Wea. Rev. 133, 604-620.
    • Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E. and Patil, D. J. and coauthors. 2004. Four-dimensional ensemble Kalman filtering. Tellus 56A, 273-277.
    • IEEE, 1985. IEEE Standard for Binary Floating-Point Arithmetic. ANSI/IEEE Std. 754-1985. American National Standards Institute.
    • James, I. N. 1994 Introduction to Circulating Atmospheres. Cambridge Univ. Press, Cambridge.
    • Kalnay, E. 2003. Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge Univ. Press, Cambridge.
    • Keppenne, C. and Rienecker, H. 2002. Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Wea. Rev. 130, 2951- 2965.
    • Lawson, C. L., Hanson, R. J., Kincaid, D. R. and Krogh F. T. 1979. Basic Linear Algebra Subprograms for FORTRAN usage. ACM Trans. Math. Soft. 5, 308-323.
    • Lorenz, E. N. 1996. Predictability: a problem partly solved. In Proc. Seminar on Predictability, Vol. 1. European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, RG2 9AX, UK.
    • 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.
    • Lynch, P. 2002. The swinging spring: a simple model of atmospheric balance. In: Large-scale Atmosphere-Ocean Dynamics II (eds. J. Norbury, and I. Roulstone). Cambridge Univ. Press, Cambridge.
    • Lynch, P. and Huang, P. M. 1992. Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev. 120, 1019-1034.
    • Machenauer, B. 1997. On the dynamics of gravity oscillation in a shallow-water model, with application to normal mode initialization. Beitr. Phys. Atmos. 50, 253-251.
    • Menard, R., Cohn, S. E., Chang, L-P. and Lyster, P. M. 2000. Assimilation of stratospheric chemical tracer observations using a Kalman Filter. Part I: Formulation. Mon. Wea. Rev. 128, 2654-2670.
    • Oczkowski, M., Szunyogh, I., Patil, D. J., and Zimin, A. V. 2005. Mechanisms for the development of locally low dimensional atmospheric dynamics. J. Atmos. Sci. 65, 1135-1156.
    • Ott, E., Hunt, B. H., Szunyogh, I., Corazza, M., Kalnay, E. and co-authors. 2002. Exploiting local low dimensionality of the atmospheric dynamics for efficient Kalman filtering. Preprint (physics/0203058).
    • Ott, E., Hunt, B. H., Szunyogh, I., Zimin, A. V., Kostelich, E. J. and coauthors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415-428.
    • Pan, H-L. and Wu, W-S. 1995. Implementing a Mass Flux Convection Package for the NMC Medium-Range Forecast model. NMC Office Note 409. Available from NCEP, 5200 Auth Road, Washington, 20233.
    • Patil, D. J., Hunt, B. R., Kalnay, E., Yorke, J. A. and Ott, E. 2001. Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett. 86, 5878-5881.
    • Snyder, C. and Zhang, F. 2003. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev. 131, 1663-1677.
    • Szunyogh, I., and Toth, Z. 2002. The effect of increased horizontal resolution on the NCEP global ensemble mean forecasts. Mon. Wea. Rev. 130, 1125-1143.
    • Tippett, M. K., Anderson, J. L., Bishop, C. H., Hammill, T. M. and Whitaker, J. S. 2002. Ensemble square-root filters. Mon. Wea. Rev. 131, 1485-1490.
    • Whitaker, J. S. and Hamill, T. H. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1913- 1924.
    • 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.
    • Zhang, F., Snyder, C. and Sun, J. 2004. Impacts of initial estimate and observation availability on convective-scale dataassimilation with an ensemble Kalman filter. Mon. Wea. Rev. 132, 1238-1253.
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