A non-Gaussian ensemble filter for assimilating infrequent noisy observations

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 59, Issue 2, 2007, Pages 225-237

  Harlim, John ^a,b   Hunt, Brian R ^a  

^a University of Maryland   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; FILTER; FORECASTING METHOD; GAUSSIAN METHOD; KALMAN FILTER; NOISE; OPTIMIZATION; SIGNAL-TO-NOISE RATIO;

EID: 34247587126     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2007.00225.x     Document Type: Article

References (28)

1. Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Weather Rev. 129, 2884-2903.
2. Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Weather Rev. 127, 2741-2758.
3. Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Weather Rev. 129, 420-436.
4. Cohn, S. E., da Silva, A., Sienkiewicz, M and Lamich, D. 1998. Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Weather Rev. 126, 2913-2926.
5. Courtier, P., Andersson, E., Heckley, W., Pailleux, J., Vasiljevic, D., Hamrud, M., Hollingsworth, A., Rabier, F. and Fisher, M. 1998. The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation. Qtr. J. R. Meteorol. Soc. 124, 1783-1807.
6. Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10143-10162.
7. Evensen, G. 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53, 343-367.
8. Fisher, M. and Courtier, P. 1995. Estimating the covariance matrix of analysis and forecast error in variational data assimilation. ECMWF Tech. Memo. 220.
9. Fletcher, S. J. and Zupanski, M. 2006. A data assimilation method for log-normally distributed observational errors. Quarterly J. R. Meteorol. Soc. 132, 2505-2519.
10. Ghil, M., Cohn, S. E., Tavantzis, J., Bube, K. and Isaacson, E. 1981. Applications of estimation theory to numerical weather prediction. In: Dynamic meteorology: Data assimilation methods, (eds. L. Bengtsson, M. Ghil and E. Källén), Springer-Verlag, New York, 139-224.
11. Hamill, T. M. and Whitaker, J. S. 2005. Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Weather Rev. 133, 3132-3147.
12. Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 126, 796-811.
13. Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 129, 123-137.
14. Keppenne, C. 2000. Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Weather Rev. 128, 1971-1981.
15. Lorenc, A. C. 1986. Analysis methods for numerical weather prediction. Quarterly J. R. Meteorol. Soc. 112, 1177-1194.
16. Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130-141.
17. Lorenz, E. N. 1996. Predictability: A problem partly solved. In: Proceeding seminar on predictability, Vol. 1, ECMWF, Reading, Berkshire, UK, 1-18.
18. Lorenz, E. N. and Emanuel, K. A. 1998. Optimal sites for supplementary weather observations: Simulation with a small model. J. Atm. Sci. 55, 399-414.
19. Mitchell, H. L., Houtekamer, P. L. and Pellerin, G. 2002. Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Weather Rev. 130, 2791-2808.
20. Molteni, F. 2003. Atmospheric simulations using a GCM with simplified physical parametrizations, I: Model climatology and variability in multi-decadal experiments. Clim. Dyn. 20, 175-191.
21. Nusse, H. E. and Yorke, J. A. 1997. Dynamics: Numerical explorations, Applied Mathematical Sciences 101. Springer-Verlag, New York, 2nd edition, book includes a computer disk with the program "Dynamics".
22. Ott, E., Hunt, B.R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corrazza, M., Kalnay, E., Patil, D. J. and Yorke, J. A. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415-428.
23. Szunyogh, I., Kostelich, E. J., Gyarmati, G., Patil, D. J., Hunt, B. R., Kalnay, E., Ott, E. and Yorke, J. A. 2005. Assessing a local ensemble Kalman filter: Perfect model experiments with the National Centers for Environmental Prediction global model. Tellus 57A, 528-545.
24. Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. 2003. Ensemble square-roots filters. Mon. Weather Rev. 131, 1485-1490.
25. van Leeuwen, P. J. 1999. Comment on "Data assimilation using an ensemble Kalman filter technique". Mon. Weather Rev. 127, 1374-1377.
26. Wang, X., Bishop, C. H. and Julier, S. J. 2004. Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Weather Rev. 132, 1590-1605.
27. Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Weather Rev. 130, 1913-1924.
28. Zupanski, M. 2005. Maximum likelihood ensemble filter: Theoretical aspects. Mon. Weather Rev. 133, 1710-1726.