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
Köhl, Armin; Willebrand, Jürgen (2011)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
The study investigates perspectives of the parameter estimation problem with the adjoint methodin eddy-resolving models. Sensitivity to initial conditions resulting from the chaotic nature ofthis type of model limits the direct application of the adjoint method by predictability.Prolonging the period of assimilation is accompanied by the appearance of an increasing numberof secondary minima of the cost function that prevents the convergence of this method. In theframework of the Lorenz model it is shown that averaged quantities are suitable for describinginvariant properties, and that secondary minima are for this type of data transformed intostochastic deviations. An adjoint method suitable for the assimilation of statistical characteristicsof data and applicable on time scales beyond the predictability limit is presented. The approachassumes a greater predictability for averaged quantities. The adjoint to a prognostic model forstatistical moments is employed for calculating cost function gradients that ignore the finestructure resulting from secondary minima. Coarse resolution versions of eddy-resolving modelsare used for this purpose. Identical twin experiments are performed with a quasigeostrophicmodel to evaluate the performance and limitations of this approach in improving models byestimating parameters. The wind stress curl is estimated from a simulated mean stream function.A very simple parameterization scheme for the assimilation of second-order moments is shownto permit the estimation of gradients that perform efficiently in minimizing cost functions.DOI: 10.1034/j.1600-0870.2002.01294.x
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Boffetta, G., Giuliani, P., Paladin, G. and Vulpiani, A. 1998. An extension of the Lyapunov analysis for the predictability problem. J. Atmos. Sci. 23, 3409-3416.
    • Courtier, P., The´paut, J. N. and Hollingsworth, A. 1994. A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc. 120, 1367-1387.
    • Eckmann, J.-P. 1981. Roads to turbulence in dissipative dynamical systems. Rev. Mod. Phys. 53, 643-654.
    • Evensen, G. 1992. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. J. Geophys. Res. 97, 17,905-17,924.
    • Evensen, G. 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys Res. 99, 10,143-10,162.
    • Evensen, G. and van Leeuwen, P. J. 1996. Assimilation of Geosat altimeter data for the Agulhas Current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev. 124, 85-96.
    • Farge, M. 1992. Wavelet transforms and their applications to turbulence. Ann. Rev. Fluid. Mech. 24, 395-457.
    • Fox, A., Haines, K., de Cuevas, B. and Webb, D. 1998. Assimilation of TOPEX data into the OCCAM model. Int. WOCE Newsletter 31, 12-15.
    • Gauthier, P. 1992. Chaos and quadri-dimensional data assimilation: a study based on the Lorenz model. T ellus 44A, 2-17.
    • Green, J. S. A. 1970. Transfer properties of the largescale eddies and the general circulation of the atmosphere. Q. J. R. Meteorol. Soc. 96, 157-417.
    • Griffies, S. M. and Bryan, K. 1997. Predictability of North Atlantic multidecadal climate variability. Science 275, 181-184.
    • Holland, W. R. 1978. The role of mesoscale eddies in the general circulation of the ocean - Numerical experiments using a wind-driven quasi-geostrophic model. J. Phys. Oceanogr. 8, 363-392.
    • Holloway, G. and Hendershott, M. C. 1977. Stochastic closure for nonlinear Rossby waves. J. Fluid Mech. 82, 747-765.
    • Jung, T., Ruprecht, E. and Wagner, F. 1998. Determination of cloud liquid water path over the oceans from Special Sensor Microwave/Imager (SSM/I) data using neural networks. J. Applied Met. 37, 832-844.
    • Killworth, P. D., Dietrich, C., Provost, C. L., Oschlies, A. and Willebrand, J. 2001. Assimilation of altimetric data and mean sea surface height into an eddy-permitting model of the North Atlanic. Progr. Oceanogr. 48, 313-335.
    • Le Dimet, F.-X. and Talagrand, O. 1986. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. T ellus 38A, 97-110.
    • Lea, D. J., Myles, A. and Haine, T. W. N. 2000. Sensitivity analysis of the climate of a chaotic system. T ellus 52A, 523-532.
    • Li, Y. 1991. A note on the uniqueness problem of variational adjustment approach to four-dimensional data assimilation. J. Meteotol. Soc. Jpn. 69, 581-585.
    • Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130-141.
    • Lorenz, E. N. 1975. Climate predictability: the physical basis of climate modeling. W MO, GARP Pub. Ser. 16, 132-136.
    • Marotzke, J. and Wunsch, C. 1993. Finding the steady state of a general circulation model through data assimilation: application to the North Atlantic Ocean. J. Geophys. Res. 98, 20,149-20,167.
    • Meacham, S. P. 2000. Low frequency variability in the wind-driven circulation. J. Phys. Oceanogr. 30, 269-293.
    • Miller, R. N., Carter, E. F. and Blue, S. T. 1999. Data assimilation into nonlinear stochastic models. T ellus 51A, 167-194.
    • Miller, R. N., Ghil, M. and Gauthiez, F. 1994. Advanced data assimilation in strongly nonlinear dynamical systems. J. Atmos. Sci. 51, 1037-1056.
    • Moore, A. M. 1991. Data assimilation in a quasi-geostrophic open-ocean model of the Gulf Stream region using the adjoint method. J. Phys. Oceanogr. 21, 398-427.
    • Nese, J. M., Dutton, J. A. and Wells, R. 1987. Calculated attractor dimensions for low-order spectral models. J. Atmos. Sci. 44, 1950-1972.
    • Oschlies, A. and Willebrand, J. 1996. Assimilation of Geosat altimeter data into an eddy-resolving primitive equation model of the North Atlantic Ocean. J. Geophys. Res. 101, 14,175-14,190.
    • Oseledec, V. I. 1968. A multiplicative ergodic theorem. Lyapunov characteristic numbers for dynamical systems. T rans. Moscow Math. Soc. 19, 197-231.
    • Palmer, T. N. 1993. Extended-range atmospheric prediction and the Lorenz model. Bull. Am. Meteorol. Soc. 74, 49-65.
    • Palmer, T. N. 1996. Predictability of the atmosphere and oceans: from days to decades. In: Decadal climate variability (eds. D. T. L. Anderson and J. Willebrand), NAT O ASI Series 44, 83-156.
    • Pires, C., Vautard, R. and Talagrand, O. 1996. On extending the limits of variational assimilation in nonlinear chaotic systems. T ellus 48A, 96-121.
    • Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. 1993. Numerical recipes in Fortran. Cambridge University Press, 963 pp.
    • Rabier, F., The´paut, J. N. and Courtier, P. 1998. Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Q. J. R. Meteorol. Soc. 124, 1861-1887.
    • Risken, H. 1984. T he Fokker-Planck equation: methods of solution and applications. Springer-Verlag, Berlin, 545 pp.
    • Schiller, A. and Willebrand, J. 1995. The mean circulation of the Atlantic Ocean north of 30S determined with the adjoint method applied to an ocean general circulation model. J. Marine Res. 53, 453-497.
    • Schr o¨ter, J., Seiler, U. and Wenzel, M. 1993. Variational assimilation of Geosat data into an eddy resolving model of the Gulf Stream extension area. J. Phys. Oceanogr. 23, 925-953.
    • Sirkes, Z. and Tziperman, E. 1997. Finite difference of adjoint or adjoint of finite difference? Mon. Wea. Rev. 125, 3373-3378.
    • Stammer, D. 1997. Global characteristics of ocean variability estimated from regional TOPEX/POSEIDON altimeter measurements. J. Phys. Oceanogr. 27, 1743-1768.
    • Stensrud, D. J. and Bao, J. W. 1992. Behaviors of variational and nudging techniques with a chaotic loworder model. Mon. Wea. Rev. 120, 3016-3028.
    • Stone, P. H. 1972. A simplified radiative-dynamical model for the static stability of rotating atmospheres. J. Atmos. Sci. 29, 405-418.
    • Tanguay, M., Bartello, P. and Gauthier, P. 1995. Fourdimensional assimilation with a wide range of scales. T ellus 47A, 947-967.
    • Thacker, W. C. 1986. Relationships between statistical and deterministic methods of data assimilation. Variational methods in geosiences (ed. Y. K. Sasaki). Elsevier, Amsterdam.
    • Thacker, W. C. 1989. The role of the Hessian matrix in fitting models to measurements. J. Geophys. Res. 94, 6177-6196.
    • Vogeler, A. and Schr o¨ter, J. 1995. Assimilation of satellite altimeter data into an open ocean model. J. Geophys. Res. 100, 15,951-15,963.
    • Yu, L. and Malanotte-Rizzoli, P. 1998. Inverse modeling of seasonal variations in the North Atlantic Ocean. J. Phys. Oceanogr. 28, 902-922.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from