Balance and ensemble Kalman filter localization techniques

Monthly Weather Review

Volumn 139, Issue 2, 2011, Pages 511-522

  Greybush, Steven J ^a   Kalnay, Eugenia ^a   Miyoshi, Takemasa ^a   Ide, Kayo ^a   Hunt, Brian R ^a  

^a Bldg 142   (United States)

Author keywords

Data assimilation; Ensembles; Kalman filters; Local effects; Model comparison

Indexed keywords

BACKGROUND-ERROR COVARIANCES; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ENSEMBLES; ERROR COVARIANCES; GEOSTROPHIC BALANCE; GLOBAL ATMOSPHERIC MODELS; GRID POINTS; INITIAL CONDITIONS; LENGTH SCALE; LOCAL EFFECTS; LOCAL REGION; LOCALIZATION TECHNIQUE; MODEL COMPARISON; NUMERICAL WEATHER PREDICTION; OBSERVATION ERRORS; PARALLEL IMPLEMENTATIONS; PARAMETERIZATIONS; PRIMITIVE EQUATIONS; SHALLOW WATER EQUATIONS; SLOW MANIFOLDS; WIND SPEED;

ATMOSPHERICS; COVARIANCE MATRIX; DATA PROCESSING; KALMAN FILTERS; OPTIMIZATION;

TRACKING (POSITION);

AGEOSTROPHIC FLOW; ATMOSPHERIC MODELING; CORRELATION; COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; GRAVITY WAVE; KALMAN FILTER; NUMERICAL METHOD; PARAMETERIZATION; SHALLOW-WATER EQUATION; WAVE PROPAGATION; WEATHER FORECASTING;

EID: 79953216060     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3328.1     Document Type: Article

References (29)

1. Amezcua, J., E. Kalnay, and P. Williams, 2011: The effects of the RAWfilter on the climatology and forecast skill of the SPEEDY model. Mon. Wea. Rev., 139, 608-619.
2. Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
3. 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.
4. Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2:Astrategy for the atmosphere. Tellus, 61A, 97-111.
5. Campbell, W. F., C. H. Bishop, and D. Hodyss, 2010: Vertical covariance localization for satellite radiances in ensemble Kalman filters. Mon. Wea. Rev., 138, 282-290.
6. Cohn, S. E., A. da Silva, J. Guo, M. Sienkiewicz, and D. Lamich, 1998: Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Wea. Rev., 126, 2913-2926.
7. Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.
8. 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), 10 143-10 162.
9. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
10. 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.
11. Holland, B. W., and X. Wang, 2010: A comparison of the local ensemble transform Kalman filter and ensemble square root filter data assimilation schemes. Preprints, 14th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), Atlanta, GA, Amer. Meteor. Soc., 11.1. [Available online at http://ams.confex.com/ams/90annual/techprogram/paper_165394.htm.].
12. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
13. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
14. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
15. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman Filter. Physica D, 230, 112-126.
16. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., Trans. ASME, 82, 35-45.
17. Kepert, J., 2009: Covariance localisation and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1157-1176.
18. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter forNWP-Acomparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
19. Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 1019-1034.
20. 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.
21. Miyoshi, T., 2005: Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, 226 pp.
22. Miyoshi, T.,and Y. Sato, 2007: Assimilating satellite radiances with a local ensemble transform Kalman filter (LETKF) applied to the JMA global model (GSM). SOLA, 3, 37-40.
23. Miyoshi, T., and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861.
24. Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments. Climate Dyn., 20, 175-191.
25. Patil, D. J., B. R. Hunt, E. Kalnay, J. A. Yorke, and E. Ott, 2001: Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett., 86, 5878-5881.
26. Raymond, D. J., 1992: Nonlinear balance and potential-vorticity thinking at large Rossby number. Quart. J. Roy. Meteor. Soc., 118, 987-1015.
27. Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield, and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A, 113-130.
28. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
29. Williams, P. D., 2009: A proposed modification to the Robert-Asselin time filter. Mon. Wea. Rev., 137, 2538-2546.