Advances in sequential estimation for atmospheric and oceanic flows

Journal of the Meteorological Society of Japan

Volumn 75, Issue 1 B, 1997, Pages 289-304

  Ghil, Michael ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001198970     PISSN: 00261165     EISSN: None     Source Type: Journal    
DOI: 10.2151/jmsj1965.75.1B_289     Document Type: Article

References (22)

1. Anderson, B.D.O. and J.B. Moore, 1979: Optimal Filtering, Prentice-Hall, 357 pp.
2. Aref, H., 1984: Integrable, chaotic, and turbulent vortex motion in two-dimensional flow. Ann. Rev. Fluid Mech., 15, 345-389.
3. Balgovind, R., A. Dalcher, M. Ghil and E. Kalnay, 1983: A stochastic-dynamic model for the spatial structure of forecast error statistics. Mon. Wea. Rev., 111, 701-722.
4. Barth, N., 1992: Oceanographic experiment design II: Genetic algorithms. J. Atmos. Ocean Tech., 9, 434-443.
5. Barth, N. and C. Wunsch, 1990: Oceanographic experiment design by simulated annealing. J. Phys. Oceanogr., 20, 1249-1263.
6. Bengtsson, L. and J. Shukla, 1988: Integration of space and in situ observations to study global climate change. Bull. Amer. Meteor. Soc., 69, 1130-1143.
7. Bennett, A.F., 1992: Inverse Methods in Physical Oceanography. Cambridge Univ. Press, Cambridge, UK, 346 pp.
8. Bennett, A.F., B.S. Chua and L.M. Leslie, 1997: Generalized inversion of a global numerical weather prediction model, II: Analysis and implementation. Meteorol. Atmos. Phys., in press.
9. Bergthorsson, P. and B.R. Döös, 1955: Numerical weather map analysis: Tellus, 7, 329-340.
10. Bierman, G.J., 1977: Factorization Methods for Discrete Sequential Estimation, Academic Press, Orlando, FL, 241 pp.
11. Boggs, D., M. Ghil and C. Keppenne, 1995: A stabilized sparse-matrix U-D square-root implementation of a large-state extended Kalman filter. In Proceedings of the Second WMO International Symposium on Assimilation of Observations in Meteorology and Oceanography, Tokyo, March 1995, WMO/TD-No. 651, PWPR Report Series No. 5, World Meteorological Organization, Geneva, Switzerland, Vol. I, pp. 219-224.
12. Bouttier, F., 1994: A dynamical estimation of error covariances in an assimilation system. Mon. Wea. Rev., 122, 2376-2390.
13. Bucy, R.S. and P.D. Joseph, 1987: Filtering for Stochastic Processes with Application to Guidance, 2nd ed., Chelsea, New York, 217 pp.
14. Buizza, R., J. Tribbia, F. Molteni and T. Palmer, 1993: Computation of optimal unstable structures for a numerical weather prediction model. Tellus, 45A, 388-406.
15. Caitlin, D.E., 1989: Estimation, Control and the Discrete Kalman Filter. Springer-Verlag, New York/Berlin/Heidelberg/London/Tokyo, 274 pp.
16. Cane, M.A., S.E. Zebiak and S.C. Dolan, 1986: Experimental forecasts of El Nin̄o. Nature, 321, 827-832.
17. Charney, J.G., M. Halem and R. Jastrow, 1969: Use of incomplete historical data to infer the present state of the atmosphere. J. Atmos. Sci., 26, 1160-1163.
18. Cohn, S.E., 1993: Dynamics of short-term univariate forecast error covariances. Mon. Wea. Rev., 121, 3123-3149.
19. Cohn, S.E., 1997: Estimation theory for data assimilation problems: Basic conceptual framework and some open questions. J. Meteor. Soc. Japan, 75, 257-288.
20. Cohn, S. E. and D.F. Parrish, 1991: The behavior of forecast error covariances for a Kalman filter in two dimensions. Mon. Wea. Rev., 119, 1757-1785.
21. Cohn, S.E., N.S. Sivakumaran and R. Todling, 1994: A fixed-lag Kalman smoother for retrospective data assimilation. Mon. Wea. Rev., 122, 2838-2867.
22. Courtier, P., 1997: Variational methods. J. Meteor. Soc. Japan, 75, 211-218.