What constrains spread growth in forecasts initialized from ensemble Kalman filters?

Monthly Weather Review

Volumn 139, Issue 1, 2011, Pages 117-131

  Hamill, Thomas M ^a   Whitaker, Jeffrey S ^a  

^a EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

Ensembles; Kalman filters; Model errors; Model evaluation performance; Model initialization; Numerical weather prediction forecasting; Primitive equation model; Spectral analysis models distribution

Indexed keywords

ENSEMBLES; MODEL ERRORS; MODEL INITIALIZATION; NUMERICAL WEATHER PREDICTION/FORECASTING; PRIMITIVE EQUATION MODEL; SPECTRAL ANALYSIS/MODELS/DISTRIBUTION;

ADDITIVE NOISE; DATA PROCESSING; ERRORS; EXPERIMENTS; KALMAN FILTERS; WEATHER FORECASTING;

MATHEMATICAL MODELS;

DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER; NUMERICAL MODEL; SPECTRAL ANALYSIS; WEATHER FORECASTING;

EID: 79951643933     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3246.1     Document Type: Article

References (74)

1. Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805-1824.
2. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
3. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for enemble filters. Tellus, 61, 72-83.
4. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.
5. Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 1452-1463.
6. Berner, J., G. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603-626.
7. 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.
8. Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2: Astrategy for the atmosphere. Tellus, 61A, 97-111.
9. Bougeault, P., and Coauthors, 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 1059-1072.
10. Buehner, M., and M. Charron, 2007: Spectral and spatial localization of background-error correlations for data assimilation. Quart. J. Roy. Meteor. Soc., 133, 615-630.
11. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 1550-1566.
12. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: 1-month experiments with real observations. Mon. Wea. Rev., 138, 1567-1586.
13. Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 2887- 2908.
14. Buizza, R., M. Leutbecher, and L. Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 134, 2051-2066.
15. 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.
16. Charron, M., G. Pellerin, L. Spacek, P. L. Houtekamer,N. Gagnon, H. L. Mitchell, and L. Michelin, 2010: Towards random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 1877-1901.
17. Compo, G. P., J. S. Whitaker, and P. D. Sardeshmukh, 2006: Feasibility of a 100-year reanalysis using only surface pressure data. Bull. Amer. Meteor. Soc., 87, 175-190.
18. Courtier, P., J.-N. Thepaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
19. Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.
20. Danforth, C. M., E. Kalnay, and T. Miyoshi, 2007: Estimating and correcting global weather model error. Mon. Wea. Rev., 135, 281-299.
21. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323-3343.
22. Ehrendorfer, M., 2007: A review of issues in ensemble Kalman filtering. Meteor. Z., 16, 795-818.
23. Evensen, G., 1994: Sequential data assimilation with an nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
24. Evensen, G., 2006: Data Assimilation: The Ensemble Kalman Filter. Springer Press, 285 pp.
25. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
26. Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155-167.
27. Hamill, T. M., 2006: Ensemble-based atmospheric data assimilation. Predictability of Weather and Climate, T. N. Palmer and R. Hagedorn, Eds., Cambridge University Press, 124-156.
28. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter/3D-variational analysis scheme. Mon. Wea. Rev., 128, 2905- 2919.
29. Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Wea. Rev., 133, 3132- 3147.
30. Hamill, T. M., and J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimate in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
31. Hamill, T. M., C. Snyder, and R. Morss, 2002: Analysis-error statistics of a quasigeostrophic model using three-dimensional variational assimilation. Mon. Wea. Rev., 130, 2777-2790.
32. Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. J. Benjamin, 2011: Global ensemble predictions of 2009's tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., in press.
33. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
34. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
35. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
36. Houtekamer, P. L., and H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
37. Houtekamer, P. L., and H. L. Mitchell, and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137, 2126-2143.
38. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2006: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126, doi:10.1016/j.physd.2006.11.008.
39. Judd, K., and L. Smith, 2001: Indistinguishable states II: The imperfect model scenario. Physica D, 196, 224-242.
40. Kalnay, E., B. Hunt, E. Ott, and I. Szunyogh, 2006: Ensemble forecasting and data assimilation: Two problems with the same solution? Predictability of Weather and Climate, T. N. Palmer and R. Hagedorn, Eds., Cambridge University Press, 157-180.
41. Kepert, J. D., 2009: Covariance localization and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1157- 1176.
42. Klinker, E., and P. D. Sardeshmukh, 1992: The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608-627.
43. Lawson, W. G., and J. A. Hansen, 2004: Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Wea. Rev., 132, 1966-1981.
44. Le Dimet, F.-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations. Theoretical aspects. Tellus, 38A, 97-110.
45. Lee, S., and I. M. Held, 1993: Baroclinic wave packets in models and observations. J. Atmos. Sci., 50, 1413-1428.
46. Li, H., E. Kalnay, T. Miyoshi, and C. M. Danforth, 2009: Accounting for model errors in ensemble data assimilation. Mon. Wea. Rev., 137, 3407-3419.
47. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP - A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
48. Lorenz, E. N., 1993: The Essence of Chaos. University of Washington Press, 227 pp.
49. Meng, Z., and F. Zhang, 2008a: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVar in a real-data case study. Mon. Wea. Rev., 136, 522-540.
50. Meng, Z., and F. Zhang, 2008b: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVar in a month-long experiment. Mon. Wea. Rev., 136, 3671-3682.
51. Mitchell, H. L., and P. L. Houtekamer, 2000: Anadaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416-433.
52. Mitchell, H. L., and P. L. Houtekamer, 2009: Ensemble Kalman filter configurations and their performance with the logistic map. Mon. Wea. Rev., 137, 4325-4343.
53. Mitchell, H. L., and 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.
54. 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.
55. Palmer, T. N., 2002: Predicting uncertainty in numerical forecasts. Meteorology at the Millennium, R. P. Pearce, Ed., International Geophysical Series, Vol. 83, Academic Press, 3-13.
56. Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher,G. J. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic parametrization andmodel uncertainty. ECMWF Tech. Memo. 598, 42 pp. [Available online at http://www.ecmwf.int/publications/library/do/references/list/14.]
57. Parrish, D., and J. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
58. Rabier, F., H. Jarvinen,E.Klinker, J.-F.Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170.
59. Sakov, P., and P. R. Oke, 2008: Implications of the form of the ensemble transformation in the ensemble square-root filters. Mon. Wea. Rev., 136, 1042-1053.
60. Shutts, G. J., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102.
61. Snyder, C., and T. M. Hamill, 2003: Leading Lyapunov vectors of a turbulent baroclinic jet in a quasigeostrophic model. J. Atmos. Sci., 60, 683-688.
62. 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.
63. Torn, R. D., and G. J. Hakim, 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 3947-3963.
64. Torn, R. D., and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX, observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817-2829.
65. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull.Amer. Meteor. Soc., 74, 2317- 2330.
66. Vannitsem, S., and C. Nicolis, 1997: Lyapunov vectors and error growth patterns in a T21L3 quasigeostrophic model. J. Atmos. Sci., 54, 347-361.
67. Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5116-5131.
68. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations.Mon.Wea. Rev., 130, 1913-1924.
69. 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.
70. Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev., 136, 463-482.
71. Whitaker, J. S., G. P. Compo, and J.-N. Thépaut, 2009: A comparison of variational and ensemble-based data assimilation systems for reanalysis of sparse observations. Mon. Wea. Rev., 137, 1991-1999.
72. Wu, W. S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905-2916.
73. Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185.
74. Zhang, F., Y. Weng, Z. Meng, J. A. Sippel, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter: Humberto (2007). Mon. Wea. Rev., 137, 2105- 2125.