The evolution of the ECMWF hybrid data assimilation system

Quarterly Journal of the Royal Meteorological Society

Volumn 142, Issue 694, 2016, Pages 287-303

  Bonavita, Massimo ^a   Hólm, Elias ^a   Isaksen, Lars ^a   Fisher, Mike ^a  

^a EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

Author keywords

Ensemble data assimilation; Flow dependent background error covariances; Hybrid 4D Var

Indexed keywords

ATMOSPHERIC MOVEMENTS; COVARIANCE MATRIX; ERRORS; VALUE ENGINEERING;

BACKGROUND ERROR VARIANCES; BACKGROUND ERRORS; BACKGROUND-ERROR COVARIANCES; DATA ASSIMILATION; ENSEMBLE DATA ASSIMILATION; HYBRID 4D-VAR; OBSERVATION QUALITIES; RESEARCH AND DEVELOPMENT;

QUALITY CONTROL;

COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; NUMERICAL MODEL; QUALITY CONTROL; WEATHER FORECASTING;

EID: 84957842027     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2652     Document Type: Article

References (46)

1. Barker DM. 1999. 'The use of synoptic-dependent error structure in 3D-Var'. Met Office VAR Scientific Development paper 25. Met Office: Exeter, UK.
2. Belo Pereira M, Berre L. 2006. The use of an ensemble approach to study the background-error covariances in a global NWP model. Mon. Weather Rev. 134: 2466-2489.
3. Berre L, Pannekoucke O, Desroziers G, Stefanescu SE, Chapnik B, Raynaud L. 2007. A variational assimilation ensemble and the spatial filtering of its error covariances: Increase of sample size by local spatial averaging. In Proceedings of the Workshop on Flow-Dependent Aspects of Data Assimilation 151-168. ECMWF: Reading, UK.
4. Bishop CH, Hodyss D. 2011. Adaptive ensemble covariance localization in ensemble 4D-VAR state estimation. Mon. Weather Rev. 139: 1241-1255.
5. Bonavita M. 2014. On some aspects of the impact of GPSRO observations in global numerical weather prediction. Q. J. R. Meteorol. Soc. 140: 2546-2562, doi:10.1002/qj.2320.
6. Bonavita M, Raynaud L, Isaksen L. 2011. Estimating background-error variances with the ECMWF ensemble of data assimilation system: The effect of ensemble size and day-to-day variability. Q. J. R. Meteorol. Soc. 137: 423-434.
7. Bonavita M, Isaksen L, Hólm EV. 2012. On the use of EDA background-error variances in the ECMWF 4D-Var. Q. J. R. Meteorol. Soc. 138: 1540-1559.
8. Bonavita M, Hamrud M, Isaksen L. 2015. EnKF and hybrid gain ensemble data assimilation, part II: EnKF and hybrid gain results. Mon. Weather Rev. submitted.
9. Bouttier F, Derber J, Fisher M. 1997. The 1997 Revision of the Jb Term in 3D/4D Var, Technical Memorandum 238. ECMWF: Reading, UK.
10. Brousseau P, Berre L, Bouttier F, Desroziers G. 2011. Background-error covariances for a convective-scale data-assimilation system: AROME-France 3D-Var. Q. J. R. Meteorol. Soc. 137: 409-422.
11. Brousseau P, Berre L, Bouttier F, Desroziers G. 2012. Flow-dependent background-error covariances for a convective-scale data assimilation system. Q. J. R. Meteorol. Soc. 138: 310-322.
12. Buehner M. 2005. Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operational NWP setting. Q. J. R. Meteorol. Soc. 131: 1013-1043.
13. Buehner M, Houtekamer PL, Charette C, Mitchell HL, He B. 2010a. Intercomparison of variational data assimilation and the ensemble Kalman Filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Weather Rev. 138: 1550-1566.
14. Buehner M, Houtekamer PL, Charette C, Mitchell HL, He B. 2010b. Intercomparison of variational data assimilation and the ensemble Kalman Filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Weather Rev. 138: 1567-1586.
15. Burgers G, Van Leeuwen PJ, Evensen G. 1998. On the analysis scheme in the ensemble Kalman filter. Mon. Weather Rev. 126: 1719-1724.
16. Clayton AM, Lorenc AC, Barker DM. 2013. Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Q. J. R. Meteorol. Soc. 139: 1445-1461.
17. Daley R. 1991. Atmospheric Data Analysis. Cambridge University Press: Cambridge, UK.
18. Danforth CM, Kalnay E. 2008. Using singular value decomposition to parameterize state-dependent model errors. J. Atmos. Sci. 65: 1467-1478.
19. Dee DP, Gaspari G. 1996. 'Development of anisotropic correlation models for atmospheric data assimilation'. In Preprint Volume, 11th Conference on Numerical Weather Prediction, 19-23 August 1996. Norfolk, VA, 249-251.
20. Derber J, Bouttier F. 1999. A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus 51A: 195-221.
21. Etherton BJ, Bishop CH. 2004. Resilience of hybrid ensemble/3DVAR analysis schemes to model error and ensemble covariance error. Mon. Weather Rev. 132: 1065-1080.
22. 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, doi:10.1029/94JC00572.
23. Fisher M. 2003. Background error covariance modelling. In Proceedings of Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean: 45-63 ECMWF: Reading, UK.
24. Hamill TM, Snyder C. 2000. A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Weather Rev. 128: 2905-2919.
25. Hamrud M, Bonavita M, Isaksen L. 2014. EnKF and Hybrid Gain Data Assimilation, Technical Memorandum 733. ECMWF: Reading, UK.
26. Hólm E, Andersson E, Beljaars A, Lopez P, Mahfouf J-F, Simmons AJ, Thepaut J-N. 2002. Assimilation and Modelling of the Hydrological Cycle: ECMWF's Status and Plans, Technical Memorandum 383. ECMWF: Reading, UK.
27. Houtekamer PL, Deng X, Mitchell HL, Gagnon N, Baek S-J. 2014. Higher resolution in an operational ensemble Kalman filter. Mon. Weather Rev. 142: 1143-1162.
28. Isaksen L, Bonavita M, Buizza R, Fisher M, Haseler J, Leutbecher M, Raynaud Laure. 2010. Ensemble of Data Assimilations at ECMWF, Technical Memorandum 636. ECMWF: Reading, UK.
29. Liu C, Xiao Q, Wang B. 2008. An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev. 136: 3363-3373.
30. Lorenc AC. 2003. The potential of the ensemble Kalman filter for NWP-A comparison with 4D-Var. Q. J. R. Meteorol. Soc. 129: 3183-3203.
31. Ménétrier B, Montmerle T, Berre L, Michel Y. 2014. Estimation and diagnosis of heterogeneous flow-dependent background-error covariances at the convective scale using either large or small ensembles. Q. J. R. Meteorol. Soc. 140: 2050-2061, doi:10.1002/qj.2267.
32. Michel Y. 2013. Estimating deformations of random processes for correlation modelling: Methodology and the one-dimensional case. Q. J. R. Meteorol. Soc. 139: 771-783.
33. Pannekoucke O, Berre L, Desroziers G. 2007. Filtering properties of wavelets for local background-error correlations. Q. J. R. Meteorol. Soc. 133: 363-379.
34. Pannekoucke O, Berre L, Desroziers G. 2008. Background-error correlation length-scale estimates and their sampling statistics. Q. J. R. Meteorol. Soc. 134: 497-508.
35. Pannekoucke O, Raynaud L, Farge M. 2014. A wavelet-based filtering of ensemble background-error variances. Q. J. R. Meteorol. Soc. 140: 316-327.
36. Raynaud L, Berre L, Desroziers G. 2008. Spatial averaging of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 134: 1003-1014.
37. Raynaud L, Pannekoucke O. 2012. Heterogeneous filtering of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 138: 1589-1598.
38. Raynaud L, Berre L, Desroziers G. 2009. Objective filtering of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 135: 1177-1199.
39. Raynaud L, Berre L, Desroziers G. 2011. An extended specification of flow-dependent background-error variances in the Météo-France global 4D-Var system. Q. J. R. Meteorol. Soc. 137: 607-619.
40. Varella H, Berre L, Desroziers G. 2011. Diagnostic and impact studies of a wavelet formulation of background-error correlations in a global model. Q. J. R. Meteorol. Soc. 137: 1369-1379.
41. Wang X, Snyder C, Hamill TM. 2007a. On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Mon. Weather Rev. 135: 222-227.
42. Wang X, Hamill TM, Whitaker JS, Bishop CH. 2007b. A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes. Mon. Weather Rev. 135: 1055-1076.
43. Wang X, Barker D, Snyder C, Hamill TM. 2008. A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Weather Rev. 136: 5116-5131.
44. Wang X, Hamill TM, Whitaker JS, Bishop CH. 2009. A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales. Mon. Weather Rev 137: 3219-3232.
45. Wang X, Parrish DD, Kleist D, Whitaker JS. 2013. GSI 3DVar-based ensemble-variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Weather Rev. 141: 4098-4117.
46. Xu Q, Lu H, Gao S, Xue M, Tong M. 2008. Time-expanded sampling for ensemble Kalman filter: Assimilation experiments with simulated radar observations. Mon. Weather Rev. 136: 2651-2667.