Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model
References
-
Anderson
, J. L.. An ensemble adjustment filter for data assimilation. Mon. Wea. Rev. 129, 2884– 2903. -
Anderson
, J. L.and Anderson, S. L.. 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.. 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.and Ott, E.. Localized error bursts in estimating the state of spatiotemporal chaos. Chaos, 1042– 1049. -
Bishop
, C. H., Etherton, B. J.and Majumdar, S.. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev. 129, 420– 436. -
Bretherton
, C. S., Widmann, M., Dymnilcov, V. P., Wallace, J. M.and Blade, I.. The effective number of spatial degrees of freedom of a time-varying field. J. Climate, 1990– 2009. -
Daley
, R.. Atmospheric Data Analysis . Cambridge Univ. Press, Cambridge. -
Dee
, D. P.. 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.. 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.. 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.. 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.. Atmospheric Convection . Oxford Univ. Press, New York. -
Hamill
, T. M., Whitaker, J.and Snyder, C.. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman Filter. Mon. Wea. Rev. 129, 2776– 2790. -
Houtelcamer
, P. L.and Mitchell, H. L.. A sequential ensemble Kalman Filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123– 137. -
Houtelcamer
, 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, 133– 620. -
Hunt
, B. R., Kalnay, E., Kostelich, E. J., Ott, E.and Patil, D. J.and co-authors. 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.Introduction to Circulating Atmospheres . Cambridge Univ. Press, Cambridge. -
Kalnay
, E.. Atmospheric Modeling, Data Assimilation , and Predictability. Cambridge Univ. Press, Cambridge. -
Keppenne
, C.and Rienecker, H.. 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. Basic Linear Algebra Subprograms for FORTRAN usage. ACM Trans. Math. Soft. 5, 308– 323. -
Lorenz
, E. N.. Predictability: a problem partly solved. In Proc. Seminar on Predictability, Vol. I. European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, RG2 9AX, UK. -
Lorenz
, E. N.and Emanuel, K. A.. Optimal sites for supplementary weather observations: simulation with a small model. J. Atmos. Sci. 55, 399– 414. -
Lynch
, P.. 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.. Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev. 120, 1019– 1034. -
Machenauer
, B.. 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.. 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.. 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 co-authors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415- 428. -
Pan
, H-L.and Wu, W-S.. 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.. Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett. 86, 5878– 5881. -
Snyder
, C.and Zhang, F.. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev. 131, 1663– 1677. -
Szunyogh
, I., and Toth, Z.. 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.. Ensemble square-root filters. Mon. Wea. Rev. 131, 1485– 1490. -
Whitaker
, J. S.and Hamill, T. H.. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1913– 1924. -
Whitaker
, J. S., Compo, G. R, Wei, X.and Hamill, T. H.. Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev. 132, 1190– 1200. -
Zhang
, F., Snyder, C.and Sun, J.. Impacts of initial estimate and observation availability on convective-scale dataassimilation with an ensemble Kalman filter. Mon. Wea. Rev. 132, 1238– 1253.
DOI: https://doi.org/10.3402/tellusa.v57i4.14721 | Journal eISSN: 3035-9554
Language: English
Page range: 528 - 545
Submitted on: Jun 29, 2004
Accepted on: Jan 10, 2005
Published on: Jan 1, 2005
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
© 2005 Istvan Szunyogh, Eric J. Kostelich, G. Gyarmati, D. J. Patil, Brian R. Hunt, Eugenia Kalnay, Edward Ott, James A. Yorke, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.