Model error estimation in ensemble data assimilation

Nonlinear Processes in Geophysics

Volumn 14, Issue 1, 2007, Pages 59-71

  Gillijns, S ^a   De Moor, B ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY ASSESSMENT; DATA ASSIMILATION; ERROR ANALYSIS; MATRIX; NUMERICAL MODEL; STOCHASTICITY;

EID: 33846679917     PISSN: 10235809     EISSN: 16077946     Source Type: Journal    
DOI: 10.5194/npg-14-59-2007     Document Type: Article

References (27)

1. Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Wea. Rev., 129, 2884-2903, 2001.
2. Anderson, J. and Anderson, S.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Wea. Rev., 127, 2741-2758, 1999.
3. Bishop, C., Etherton, B., and Majundar, S.: Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects, Mon. Wea. Rev., 129, 420-436, 2001.
4. Burgers, G., van Leeuwen, P., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Wea. Rev., 126, 1719-1724, 1998.
5. Dee, D. and Da Silva, A.: Data assimilation in the presence of forecast bias, Quart. J. Roy. Meteorol. Soc., 117, 269-295, 1998.
6. Dee, D. and Todling, R.: Data assimilation in the presence of forecast bias: the GEOS moisture analysis, Mon. Wea. Rev., 128, 3268-3282, 2000.
7. Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10 143-10 162, 1994.
8. Evensen, G.: The ensemble Kalman filter: theoretical formulation and practical implementation, Ocean Dyn., 53, 343-367, 2003.
9. Friedland, B.: Treatment of bias in recursive filtering, IEEE Trans. Autom. Control, 14, 359-367, 1969.
10. Gillijns, S. and De Moor, B.: Unbiased minimum-variance input and state estimation for linear discrete-time systems, Automatica, 43, 111-116, 2007.
11. Griffith, A. and Nichols, N.: Adjoint methods in data assimilation for estimating model error, J. Flow Turbulence Combust., 65, 469-488, 2000.
12. Hamill, T. and Whitaker, J.: Accounting for the error due to unresolved scales in ensemble data assimilation: a comparison of different approaches, Mon. Wea. Rev., 133, 3132-3147, 2005.
13. Hamill, T., Whitaker, J., and Snyder, C.: Distance-dependent filtering of background-error covariance estimates in an ensemble Kalman filter, Mon. Wea. Rev., 129, 2776-2790, 2001.
14. Heemink, A., Verlaan, M., and Segers, A.: Variance reduced ensemble Kalman filtering, Mon. Wea. Rev., 129, 1718-1728, 2001.
15. Houtekamer, P. and Mitchell, H.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Wea. Rev., 129, 123-137, 2001.
16. Kailath, T., Sayed, A., and Hassibi, B.: Linear Estimation, Prentice Hall, Upper Saddle River, New Jersey, 2000.
17. Kitanidis, P.: Unbiased minimum-variance linear state estimation, Automatica, 23, 775-778, 1987.
18. Lorenz, E.: Predictability: A problem partly solved, in: Proc. ECMWF Seminar on Predictability, Vol. I, pp. 1-18, 1996.
19. Martin, M., Bell, M., and Nichols, K.: Estimation of systematic error in an equatorial ocean model using data assimilation, Int. J. Numer. Fluids, 40, 435-444, 2002.
20. Mitchell, H. and Houtekamer, P.: Ensemble size, balance, and model-error representation in an ensemble Kalman filter, Mon. Wea. Rev., 130, 2791-2808, 2002.
21. Orrell, D., Smith, L., Barkmeijer, J., and Palmer, T.: Model error in weather forecasting, Nonlin. Processes Geophys., 8, 357-371, 2001. http://www.nonlin-processes-geophys.net/8/357/2001/.
22. Ott, E., Hunt, B., Szunyogh, I., Zimin, A., Kostelich, E., Corazza, M., Kalnay, E., Patil, D., and Yorke, J.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus, 56A, 415-428, 2002.
23. Reichle, R., McLaughlin, D., and Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter, Mon. Wea. Rev., 130, 103-114, 2002.
24. Tippett, M., Anderson, J., Bishop, C., Hamill, T., and Whitaker, J.: Ensemble square root filters, Mon. Wea. Rev., 131, 1485-1490, 2003.
25. Whitaker, J. and Hamill, T.: Ensemble data assimilation without perturbed observations, Mon. Wea. Rev., 130, 1913-1924, 2002.
26. Zupanski, D.: A general weak constraint applicable to operational 4DVAR data assimilation systems, Mon. Wea. Rev., 125, 2274-2292, 1997.
27. Zupanski, D. and Zupanski, M.: Model error estimation employing an ensemble data assimilation approach, Mon. Wea. Rev., 134, 1337-1354, 2006.