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
Vidard, P. A.; Piacentini, A.; Le Dimet, F. -X. (2004)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
We propose a methodology for the treatment of the systematic model error in variational data assimilation. The principle of the method is to add a systematic error correction term in the model equations and to include it in the variational assimilation control vector. This method is applied to a simplified ocean circulation model in an identical twin experiment framework. It shows a noticeable improvement compared to the result of a classical variational assimilation scheme in which the systematic error is not corrected. The estimated systematic error correction term is sufficiently consistent with that needed by the model that it allows improvements not just to the analysis, but also during the forecast phase.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Adcroft, A. and Marshall, D. 1998. How slippery are piecewise-constant coastlines in numerical ocean models. Tellus 50A, 95-108.
    • Bell, M. J., Martin, J. M. and Nichols, N. K. 2001. Assimilation of data into an ocean model with systematic errors near the equator. Ocean Applications Technical Note No 27, UK Met Office. 27 p.
    • Cohn, S. E. 1997. An introduction to estimation theory. J. Meteor. Soc. Japan 75, 257-288.
    • Courtier, P. 1997. Dual Formulation of Four-Dimensional Variational Assimilation. Q. J. R. Meteorol. Soc. 123, 2449-2461.
    • Courtier, P., The´paut, J. N. and Hollingsworth, A. 1994. A startegy for operational implementation of 4d-var, using an incremental approach. Q. J. R. Meteorol. Soc. 120, 1367-1387.
    • D'Andrea, F. and Vautard, R. 2001. Reducing Systematic Errors by Empirically Correcting Model Errors. Tellus 52A, 21-41.
    • Dee, D. P. and Da Silva, A. M. 1998. Data assimilation in the presence of forecast bias. Q. J. R. Meteorol. Soc. 124, 269-295.
    • Derber, J. C. 1989. A Variational Continuous Assimilation Technique. Mon. Wea. Rew. 117, 2437-2446.
    • Derber, J. and Bouttier, F. 1999. A reformulation of the background error covariance in the ecmwf global data assimilation system. Tellus 51A, 195-221.
    • Durbiano, S. 2001. Vecteurs caracte´ristiques de mode`les oce´aniques pour la re´duction d'ordre en assimilation de donne´es. Reduced order strategy for 4d-var ocean data assimilation. PhD thesis, Universite´ Joseph Fourier (Grenoble I), December, 214 p. In French.
    • Gilbert, J.-C. and Lemare´chal, C. 1989. Some numerical experiments with variable-storage quasi-Newton algorithms. Math. Prog. 45, 407- 435.
    • Griffith, A. K. and Nichols, N. K. 2001. Adjoint methods in data assimilation for estimating model error. J. Flow Turbulence Combustion 65, 469-488.
    • Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory. Academic Press, New York.
    • Le Dimet, F.-X. and Talagrand, O. 1986. Variational algorithms for analysis and assimilation of meteorological observation: theroetical aspects. Tellus 38A, 97-110
    • Madec, G., Delecluse, P., Imbard, M. and Le´vy, C. 1998. OPA 8.1 ocean general circulation model: Reference manual. Note du ple de mode´lisation 11, IPSL, Paris, France. 91 p.
    • Molteni, F., Buizza, R., Palmer, T. N. and Petroliagis, T. 1996. The new ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 122, 73-119.
    • Nocedal, J. 1980. Updating quasi-Newton matrices with limited storage. Math. Comp. 24, 773-782
    • Sasaki, Y. 1958. An objective analysis based on the variational method. J. Meteorol. Soc. Japan 36, 77-88.
    • Vidard, A. 2001. Vers une prise en compte des erreurs mode`le en assimilation de donne´es 4D-variationnelle. Application a` un mode`le re´aliste d'oce´an. Toward the correction of model error in 4D variational data assimilation. Application to a realistic ocean model. PhD thesis, Universite´ Joseph Fourier (Grenoble I), 196 p. In French.
    • Vidard, A, Blayo, E., Le Dimet, F.-X. and Piacentini, A. 2001. 4dvariational data analysis with imperfect model. reduction of the size of control. J. Flow Turbulence Combustion 65, 489-504.
    • Vidard, A., Le Dimet, F.-X. and Piacentini, A. 2003. Optimal determination of nudging coefficient. Tellus 55A, 1-15.
    • Weaver, A. and Courtier, P. 2001. Correlation modelling on the sphere using a generalized diffusion equation. Q. J. R. Meteorol. Soc. 127, 1815-1846.
    • Zou, X., Navon, I. M. and Le Dimet, F.-X. 1992. An optimal nudging data assimilation scheme using parameter estimation. Q. J. R. Meteorol. Soc. 118, 1163-1186.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from