Empirical localization of observation impact in ensemble kalman filters

Monthly Weather Review

Volumn 141, Issue 11, 2013, Pages 4140-4153

  Anderson, Jeffrey ^a   Lei, Lili ^a  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENSEMBLE DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; GAUSSIAN FUNCTIONS; GEOPHYSICAL APPLICATIONS; OBSERVATION IMPACTS; OBSERVING SYSTEM SIMULATION EXPERIMENTS; REGRESSION COEFFICIENT; ROOT-MEAN SQUARE ERRORS;

EXPERIMENTS; KALMAN FILTERS;

ALGORITHMS;

ALGORITHM; DATA ASSIMILATION; EMPIRICAL ANALYSIS; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER;

EID: 84888414100     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-12-00330.1     Document Type: Article

References (37)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634-642.
3. Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
4. Anderson, J. L., 2009a: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83.
5. Anderson, J. L., 2009b: Ensemble Kalman filters for large geophysical applications. IEEE Contr. Syst. Mag., 29, 66-82.
6. Anderson, J. L., 2010: A non-Gaussian ensemble filter update for data assimilation. Mon. Wea. Rev., 138, 4186-4198.
7. Anderson, J. L., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 2359-2371.
8. Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 1452-1463.
9. Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283-1296.
10. Bishop, C. H., and D. Hodyss, 2007: Flow adaptive moderation of spurious ensemble correlations and its use in ensemble based data assimilation. Quart. J. Roy. Meteor. Soc., 133, 2029-2044.
11. Bishop, C. H., and D. Hodyss, 2009a: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus, 61A, 84-96.
12. Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2: Astrategy for the atmosphere. Tellus, 61A, 97-111.
13. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
14. Campbell, W. F., C. H. Bishop, and D. Hodyss, 2010: Vertical covariance localization for satellite radiances in ensembleKalman filters. Mon. Wea. Rev., 138, 282-290.
15. Chen, Y., and D. S. Oliver, 2009: Cross-covariances and localization for EnKF in multiphase flow data assimilation. Comput. Geosci., 14, 579-601, doi:10.1007/s10596-009-9174-6.
16. Emerick, A., and A. Reynolds, 2010: Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications. Comput. Geosci., 15, 251-269, doi:10.1007/s10596-010-9198-y.
17. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10143-10162.
18. Furrer, R., and T. Bengtsson, 2007: Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. Multivariate Anal., 98, 227-255.
19. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
20. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon. Wea. Rev., 139, 511-522.
21. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distancedependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
22. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
23. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
24. Jazwinski, A. H., 1970: Stochastic Processes and Filtering Theory. Academic Press, 376 pp.
25. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82D, 35-45.
26. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: "Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.
27. Kepert, J. D., 2009: Covariance localisation and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1157-1176.
28. Liu, H., J. L. Anderson, Y.-H. Kuo, and K. Raeder, 2007: Importance of forecast error multivariate correlations in idealized assimilations of GPS radio occultation data with the ensemble adjustment filter. Mon. Wea. Rev., 135, 173-185.
29. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP-A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3204.
30. Lorenz, E. N., and K. A. Emanuel, 1998: Optimal sites for supplementary weather observations: Simulation with a small model. J. Atmos. Sci., 55, 399-414.
31. Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791-2808.
32. Miyoshi, T., S. Yamane, and T. Enomoto, 2007: Localizing the error covariance by physical distance within a local ensemble transform Kalman filter (LETKF). SOLA, 3, 89-92.
33. Oke, P. R., P. Sakov, and S. P. Corney, 2007: Impacts of localization in the EnKF and EnOI: Experiments with a small model. Ocean Dyn., 57, 32-45.
34. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
35. Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789-1807.
36. Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 1190-1200.
37. Zhang, Y., and D. S. Oliver, 2010: Improving the ensemble estimate of the Kalman gain by bootstrap sampling. Math. Geosci., 42, 327-345.