LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hoang, H. S.; Baraille, R.; Talagrand, O. (2005)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
In a reduced-order adaptive filtering approach, we demonstrate a possibility to overcome two major difficulties in estimating oceanic circulation: a very high dimension of the system state and uncertainties in specification of the model error statistics. This approach is based essentially on the assumption that a particular parametrized gain matrix has been selected and the tuning parameters are adjusted by minimizing the mean prediction error. In the present paper we apply a reduced-order adaptive filter for solving the problem of assimilating altimetric sea surface height into a primitive equation model: the Miami isopycnic coordinate ocean model (MICOM). A gain structure is described which is proven to be very efficient in the twin experiments. The assimilation algorithm to be employed in the identical twin experiment is a reduced-order filter whose reduced state consists of the layer thickness. The velocity update is calculated from the geostrophic hypothesis. The gain structure for the non-adaptive filter is obtained on the basis of three principal hypotheses: (H1) analysis error for the system output is cancelled in the case of noise-free observations (as is done naturally in a standard Kalman filter for noise-free observation); (H2) conservation of linear potential vorticity; (H3) no correction for the velocity at the bottom layer. The initial values of the parameters in the gain will be selected in such a way that the filter behaves exactly as the Cooper–Haines filter (CHF) at the first data update step. It is shown that the adaptive filter, which relaxes one or several of the above hypotheses, is capable of producing the better estimates for the ocean state (layer thickness and velocity) compared to that produced by the CHF in all layers, surface or subsurface. Numerical experiments demonstrate the excellent capacity of the adaptive filter to extract useful information from surface observations for inferring the oceanic circulation in the MICOM.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Albert, R. 1972. Regression and the Moore-Penrose pseudoinverse. Academic Press, New York.
    • Amodei, L. 1995. Approximate solution to a problem of meteorological data assimilation with model error. C. R. Acad. Sci., Paris, 321, Series IIa, 1087-1094 (in French).
    • Anderson, B. D. O. and Moore, J. B. 1979. Optimal Filtering. PrenticeHall, Englewood Cliffs, NJ.
    • Baraille, R. and Filatoff, N. 1995. Miami multilayer isopycnal shallowwater model. Research Report CMO, EPSHOM/CMO, Brest, France.
    • Bennett, A. F., Chua, S. and Leslie, L. M. 1997. Generalized inversion of a Global Numerical Weather Prediction Model. Meteorol. Atmos. Phys. 60, 165-178.
    • Bleck, R. 1998. Ocean modeling in isopycnic coordinates. In: Ocean Modeling and Parameterization (eds E. P. Chassignet, and J. Verron). NATO Science Series, Kluwer, Dordrecht, 423-448.
    • Bryan, K. 1969. A numerical method for the study of the circulation of the word ocean. J. Comput. Phys. 4, 347-376.
    • Chassignet, E. 1992. Rings in numerical models of ocean general circulation: a statistical study. J. Geophys. Res. 97, 9479-9492.
    • Cohn, S. and Todling, R. 1996. Approximate data assimilation schemes for stable and unstable dynamics. J. Meteorol. Soc. Japan 74(1), 63- 75.
    • Cooper, M. and Haines, K. 1996. Altimetric assimilation with water property conservation. J. Geophys. Res. 101, 1059-1077.
    • Courtier, P. 1993. Introduction to numerical weather prediction data assimilation methods. In: Proceedings of ECMWF workshop on development in the use of satellite data in numerical weather prediction, Shinfield Park, Reading, Berkshire, 180-207.
    • Dee, D. 1991. Simplification of the Kalman filter for meteorological data assimilation. Q. J. R. Meteorol. Soc. 117, 365-384.
    • De Mey, P. and Robinson, A. R. 1987. Assimilation of altimeter eddy fields in a limited-area quasi-geostrophic model. J. Phys. Oceanogr. 17, 2280-2293.
    • Evensen, E. 1994. Sequential data assimilation with a non-linear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 10 143-10 162.
    • Fukumori, I. and Malanotte-Rizzoli, P. 1995. An approximate Kalman filter for ocean data assimilation: an example with an idealized Gulf Stream model. J. Geophys. Res. 100(C4), 6777-6793.
    • Fukumori, I., Benveniste, J., Wunsch, C. and Haivogel, D. 1993. Assimilation of sea surface topography into an ocean circulation model using a steady- state smoother. J. Phys. Oceanogr. 23, 1831-1855.
    • Gavart, M. 2001. Assimilation in a basin model and regional modelisation. Research Report CMO, EPSHOM/CMO, Brest, France.
    • Gavart, M. and De Mey, P. 1997. Isopycnal EOFs in the Azores Current region: a statistical tool for dynamical analysis and data assimilation. J. Phys. Oceanogr. 27, 2146-2157.
    • Ghil, M. and Malanotte-Rizzoli, P. 1991. Data assimilation in meteorology and oceanography. In: Advances in Geophysics, Vol. 33. Academic Press, New York, 141-266.
    • Golub, G. H. and Van Loan, C. 1993. Matrix Computation. Johns Hopkins Univ. Press.
    • Haines, K., Malanotte-Rizzoli, P., Young, R. E. and Holland, W. R. 1993. A comparison of two methods for the assimilation of altimeter data into a shallow water model. Dyn. Atmos. Oceans, 19, 89-133.
    • Hoang, H. S., Baraille, R., Talagrand, O., De Mey, P. and Carton, X. 1997a. Adaptive filtering: application to satellite data assimilation in oceanography. J. Dyn. Atmos. Oceans 27, 257-281.
    • Hoang, H. S., De Mey, P., Talagrand, O. and Baraille, R. 1997b. A new reduced-order adaptive filter for state estimation in high dimensional systems. Automatica 33, 1475-1498.
    • Hoang, H. S., Baraille, R., Cohn, S. E. and Talagrand, O. 2000. On stability of the partial singular value decomposition filter for stable and unstable dynamics. In: Proceedings of the International Conference on Mathematical Theory Networks and Systems (MTNS), June, Perpignan, France (available at http://www.univ-perp.fr/ mtns2000/articles/B274.pdf).
    • Hoang, H. S., Baraille, R. and Talagrand, O. 2001. On the design of a stable adaptive filter for high dimensional systems. Automatica 37, 341-359.
    • Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory. Academic Press, New York.
    • Kailath, T. 1980. Linear Systems. Prentice-Hall, Englewood Cliffs, NJ.
    • Le Dimet, F.-X. and Talagrand, O. 1986. Variational algorithm for analysis and assimilation of meteorological observations. Tellus 38A, 97- 110.
    • Ljung, L. A. 1987. System Identification. Prentice Hall, Englewood Cliffs, NJ.
    • Mellor, G. L. and Ezer, T. 1991. A Gulf stream model and an altimetry assimilation scheme. J. Geophys. Res. 96(C5), 8779-8795.
    • Pham, D. T., Verron, J. and Roubaud, M. C. 1997. Singular evolutive Kalman filter with EOF initialization for data assimilation in oceanography. J. Mar. Syst. 16, 323-340.
    • Polyak, B. T. 1990. New method of stochastic approximation type. Autom. Remote Contr. 51, 937-946.
    • Talagrand, O. and Courtier, P. 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation, I. Theory. Q. J. R. Meteorol. Soc. 113, 1311-1328.
    • Todling, R. and Cohn, S. 1994. Suboptimal schemes for atmospheric data assimilation based on the Kalman filter. Mon. Wea. Rev. 122, 2530-2557.
    • Tsypkin, Ya. 1971. Adaptation and Learning in Automatic Systems. Academic Press, New York.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from