Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP

Monthly Weather Review

Volumn 143, Issue 1, 2015, Pages 212-229

  Lorenc, Andrew C ^a   Bowler, Neill E ^a   Clayton, Adam M ^a   Pring, Stephen R ^a   Fairbairn, David ^b  

^a MET OFFICE   (United Kingdom)

^b UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; METEOROLOGY;

BACKGROUND-ERROR COVARIANCES; DATA ASSIMILATION METHODS; FOUR DIMENSIONAL VARIATIONAL; FOUR-DIMENSIONAL VARIATIONAL DATA ASSIMILATION; HIGH FREQUENCY OSCILLATIONS; INCREMENTAL ANALYSIS; OPERATIONAL METHODS; VARIATIONAL DATA ASSIMILATION;

ERRORS;

CLIMATE MODELING; CLIMATE PREDICTION; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; FOUR-DIMENSIONAL MODELING; WEATHER FORECASTING;

EID: 84921475233     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-14-00195.1     Document Type: Article

References (56)

1. 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, doi:10.1002/qj.169.
2. 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, doi:10.1111/j.1600-0870.2008.00371.x.
3. Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2:Astrategy for the atmosphere. Tellus, 61A, 97-111, doi:10.1111/j.1600-0870.2008.00372.x.
4. Bloom, S. C., L. L. Takacs, A. M. D. Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271, doi:10.1175/1520-0493(1996)124<1256: DAUIAU>.2.0.CO;2.
5. Bonavita, M., L. Isaksen, and E. Holm, 2012: On the use of EDA background error variances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 138, 1540-1559, doi:10.1002/qj.1899.
6. Bowler, N. E., A. Arribas, K. R.Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703-722, doi:10.1002/qj.234.
7. Buehner, M., 2012: Evaluation of a spatial/spectral covariance localization approach for atmospheric data assimilation. Mon. Wea. Rev., 140, 617-636, doi:10.1175/MWR-D-10-05052.1.
8. 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, doi:10.1002/qj.50.
9. 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, doi:10.1175/2009MWR3157.1.
10. 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: One-month experiments with real observations. Mon. Wea. Rev., 138, 1567-1586, doi:10.1175/2009MWR3158.1.
11. Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes Geophys., 20, 669-682, doi:10.5194/npg-20-669-2013.
12. Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2013: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Quart. J. Roy. Meteor. Soc., 139, 1445-1461, doi:10.1002/qj.2054.
13. Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994:Astrategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387, doi:10.1002/qj.49712051912.
14. Desroziers, G., J.-T. Camino, and L. Berre, 2014: 4DEnVar: Link with 4D state formulation of variational assimilation and different possible implementations. Quart. J. Roy. Meteor. Soc., 140, 2097-2110, doi:10.1002/qj.2325.
15. Etherton, B. J., and C. H. Bishop, 2004: Resilience of hybrid ensemble/3DVAR analysis schemes to model error and ensemble covariance error. Mon. Wea. Rev., 132, 1065-1080, doi:10.1175/1520-0493(2004)132<1065:ROHDAS>.2.0.CO;2.
16. Fairbairn, D., S. R. Pring, A. C. Lorenc, and I. Roulstone, 2014: A comparison of 4D-Var with ensemble data assimilation methods. Quart. J. Roy. Meteor. Soc., 140, 281-294, doi:10.1002/qj.2135.
17. Fisher, M., and H. Auvinen, 2012: Long window weak-constraint 4D-Var. ECMWF Seminar on Data Assimilation for Atmosphere and Ocean, Shinfield Park, Reading, ECMWF, 189-202.
18. Flowerdew, J., and N. E. Bowler, 2011: Improving the use of observations to calibrate ensemble spread. Quart. J. Roy. Meteor. Soc., 137, 467-482, doi:10.1002/qj.744.
19. Gauthier, P., and J.-N. Thépaut, 2001: Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo-France. Mon. Wea. Rev., 129, 2089-2102, doi:10.1175/1520-0493(2001)129<2089:IOTDFA>.2.0.CO;2.
20. Gustafsson, N., J. Bojarova, and O. Vignes, 2014: A hybrid variational ensemble data assimilation for the high resolution limited area model (HIRLAM). Nonlinear Processes Geophys., 21, 303-323, doi:10.5194/npg-21-303-2014.
21. Hamill, T.M., and C. Snyder, 2000:Ahybrid ensemble Kalman filter- 3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919, doi:10.1175/1520-0493(2000)128<2905:AHEKFV>.2.0.CO;2.
22. 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, doi:10.1175/MWR3020.1.
23. Hamill, T.M., and J. S. Whitaker, and C. Snyder, 2001: Distance dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790, doi:10.1175/ 1520-0493(2001)129<2776:DDFOBE>.2.0.CO;2.
24. Houtekamer, P., X. Deng, H. L. Mitchell, S.-J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon. Wea. Rev., 142, 1143-1162, doi:10.1175/MWR-D-13-00138.1.
25. Ide, K., P. Courtier, M. Ghil, and A. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181-189.
26. Ingleby, N. B., 2001: The statistical structure of forecast errors and its representation in the Met Office global 3D variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 127, 209-231, doi:10.1002/qj.49712757112.
27. Ingleby, N. B., A. C. Lorenc, K. Ngan, F. R. Rawlins, and D. R. Jackson, 2013: Improved variational analyses using a nonlinear humidity control variable. Quart. J. Roy. Meteor. Soc., 139, 1875- 1887, doi:10.1002/qj.2073.
28. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: 4-D-Var or ensemble Kalman filter? Tellus, 59A, 758- 773, doi:10.1111/j.1600-0870.2007.00261.x.
29. Kepert, J., 2009: Covariance localisation and balance in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1157- 1176, doi:10.1002/qj.443.
30. Kleist, D. T., and K. Ide, 2015a: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-Hybrid results. Mon. Wea. Rev., in press.
31. Kleist, D. T., and K. Ide, 2015b: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., in press.
32. Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, R. M. Errico, and R. Yang, 2009: Improving incremental balance in the GSI 3DVAR analysis system. Mon. Wea. Rev., 137, 1046-1060, doi:10.1175/2008MWR2623.1.
33. Le Dimet, F.-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97-110, doi:10.1111/ j.1600-0870.1986.tb00459.x.
34. Liu, C., Q. Xiao, and B. Wang, 2008: An ensemble-based fourdimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 3363-3373, doi:10.1175/2008MWR2312.1.
35. Liu, C., Q. Xiao, and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing System Simulation Experiments with advanced research WRF (ARW). Mon. Wea. Rev., 137, 1687-1704, doi:10.1175/ 2008MWR2699.1.
36. Lorenc, A. C., 2003a: Modelling of error covariances by fourdimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 129, 3167-3182, doi:10.1256/qj.02.131.
37. Lorenc, A. C., 2003b: The potential of the ensemble Kalman filter for NWP-A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203, doi:10.1256/qj.02.132.
38. Lorenc, A. C., 2013: Recommended nomenclature for EnVar data assimilation methods. Research Activities in Atmospheric and Oceanic Modelling, WGNE, 2 pp. [Available online at http://www. wcrp-climate.org/WGNE/BlueBook/2013/individual-articles/ 01_Lorenc_Andrew_EnVar_nomenclature.pdf.]
39. Lorenc, A. C., and F. Rawlins, 2005: Why does 4D-Var beat 3D-Var? Quart. J. Roy. Meteor. Soc., 131, 3247-3257, doi:10.1256/qj.05.85.
40. Lorenc, A. C., and Coauthors, 2000: The Met. Office global threedimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 2991-3012, doi:10.1002/ qj.49712657002.
41. Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 1019-1034, doi:10.1175/1520-0493(1992)120<1019:IOTHMU>.2.0.CO;2.
42. 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, doi:10.1175/ 1520-0493(2002)130<2791:ESBAME>.2.0.CO;2.
43. Montmerle, T., and L. Berre, 2010: Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale. Quart. J. Roy. Meteor. Soc., 136, 1408-1420, doi:10.1002/qj.655.
44. Ota, Y., J. C. Derber, E. Kalnay, and T. Miyoshi, 2013: Ensemblebased observation impact estimates using the NCEP GFS. Tellus, 65A, 20038, doi:10.3402/tellusa.v65i0.20038.
45. Polavarapu, S., S. Ren, A. Clayton, D. Sankey, and Y. Rochon, 2004: On the relationship between incremental analysis updating and incremental digital filtering. Mon. Wea. Rev., 132, 2495-2502, doi:10.1175/1520-0493(2004)132<2495: OTRBIA>.2.0.CO;2.
46. Rabier, F., 2005: Overview of global data assimilation developments in numerical weather-prediction centres. Quart. J. Roy. Meteor. Soc., 131, 3215-3233, doi:10.1256/qj.05.129.
47. Rawlins, F., S. P. Ballard, K. J. Bovis, A.M.Clayton,D. Li, G.W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global 4-dimensional variational data assimilation system. Quart. J. Roy. Meteor. Soc., 133, 347-362, doi:10.1002/qj.32.
48. Stiller, O., 2009: Efficient moist physics schemes for data assimilation. II: Deep convection. Quart. J. Roy. Meteor. Soc., 135, 721-738, doi:10.1002/qj.362.
49. Stiller, O., and S. P. Ballard, 2009: Efficient moist physics schemes for data assimilation. I: Large-scale clouds and condensation. Quart. J. Roy. Meteor. Soc., 135, 707-720, doi:10.1002/qj.400.
50. Tian, X., Z. Xie, and A. Dai, 2008: An ensemble-based explicit four-dimensional variational assimilation method. J. Geophys. Res., 113, D21124, doi:10.1029/2008JD010358.
51. Wang, X., and T. Lei, 2014: GSI-based four dimensional ensemblevariational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP global forecast system. Mon. Wea. Rev., 142, 3303-3325, doi:10.1175/ MWR-D-13-00303.1.
52. Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227, doi:10.1175/MWR3282.1.
53. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRFmodel. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131, doi:10.1175/2008MWR2444.1.
54. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132-5147, doi:10.1175/ 2008MWR2445.1.
55. Wang, X., D. Parrish, D. Kleist, and J. S. Whitaker, 2013: GSI 3DVarbased ensemble-variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Wea. Rev., 141, 4098-4117, doi:10.1175/MWR-D-12-00141.1.
56. Zhang, M., and F. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587-600, doi:10.1175/MWR-D-11-00023.1.