A posteriori diagnostics of the impact of observations on the AROME-France convective-scale data assimilation system

Quarterly Journal of the Royal Meteorological Society

Volumn 140, Issue 680, 2014, Pages 982-994

  Brousseau, Pierre ^a   Desroziers, Gérald ^a   Bouttier, François ^a   Chapnik, Bernard ^b  

^a CENTRE NATIONAL DE RECHERCHES MÉTÉOROLOGIQUES   (France)

^b DIRIC Météo France   (France)

Author keywords

AROME France 3D Var; Degrees of freedom for signal; Estimation error; Variance reduction

Indexed keywords

DEGREES OF FREEDOM (MECHANICS); MECHANICS; RADAR MEASUREMENT; THREE DIMENSIONAL; VALUE ENGINEERING; WEATHER FORECASTING;

AROME-FRANCE 3D-VAR; DATA ASSIMILATION SYSTEMS; ESTIMATION ERRORS; NUMERICAL WEATHER PREDICTION; RANDOMIZATION TECHNIQUES; SATELLITE OBSERVATIONS; TEMPERATURE AND RELATIVE HUMIDITY; VARIANCE REDUCTIONS;

GLOBAL POSITIONING SYSTEM;

AIR TEMPERATURE; AIRBORNE SURVEY; ATMOSPHERIC CONVECTION; DATA ASSIMILATION; ERROR ANALYSIS; GPS; RADAR; RELATIVE HUMIDITY; TROPOSPHERE; WEATHER FORECASTING; WIND;

EID: 84899108805     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2179     Document Type: Article

References (36)

1. Benjamin SG, Dévényi D, Weygandt S, Brundage KJ, Brown JM, Grell GA, Kim D, Schwartz BE, Smirnova TG, Smith TL. 2004. An hourly assimilation-forecast cycle: The RUC. Mon. Weather Rev. 132: 495-518.
2. Bergot T, Doerenbecher A. 2002. A study on the optimization of the deployment of targeted observations using adjoint-based methods. Q. J. R. Meteorol. Soc. 128: 1689-1712.
3. Berre L. 2000. Estimation of synoptic and mesoscale forecast error covariances in a limited area model. Mon. Weather Rev. 128: 644-667.
4. Bielli S, Grzeschik M, Richard E, Flamand C, Champollion C, Kiemle C, Dorminger M, Brousseau P. 2012. Assimilation of water-vapour airborne lidar observations: impact study on the COPS precipitation forecasts. Q. J. R. Meteorol. Soc. 138: 1652-1667.
5. Boniface K, Ducrocq V, Jaubert G, Yan X, Brousseau P, Masson F, Chéry J, Doerflinger E. 2009. Impact of high-resolution data assimilation of GPS zenith Delay Assimilation on Mediterranean heavy rainfall forecasting. Ann. Geophys. 27: 2739-2753.
6. 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.
7. 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.
8. Cardinali C, Pezzulli S, Andersson E. 2004. Influence-matrix diagnostic of a data assimilation system. Q. J. R. Meteorol. Soc. 130: 2767-2786.
9. Caumont O, Ducrocq V, Wattrelot É, Jaubert G, Pradier-Vabre S. 2010. 1D+ 3D-Var assimilation of radar reflectivity data: A proof of concept. Tellus A 62: 173187.
10. Chapnik B, Desroziers G, Rabier F, Talagrand O. 2004. Properties and first application of an error-statistics tuning method in variational assimilation. Q. J. R. Meteorol. Soc. 130: 22532275.
11. Cohn SE. 1997. An introduction to estimation theory. J. Meteorol. Soc. Japan 75: 257-288.
12. Courtier P, Thépaut J-N, Hollingsworth A. 1994. A strategy for operational implementation of 4D-Var using an incremental approach. Q. J. R. Meteorol. Soc. 120: 1367-1387.
13. Daley R. 1991. Atmospheric data analysis. Cambridge University Press: Cambridge, UK.
14. Deckmyn A, Berre L. 2005. A wavelet approach to representing background error covariances in a limited-area model. Mon. Weather Rev. 133: 1279-1294.
15. Derber J, Bouttier F. 1999. A reformulation of the background-error covariance in the ECMWF global data assimilation system. Tellus A 51: 195-221.
16. Desroziers G, Ivanov S. 2001. Diagnosis and adaptive tuning of information error parameters in a variational assimilation. Q. J. R. Meteorol. Soc. 127: 1433-1452.
17. Desroziers G, Brousseau P, Chapnik B. 2005. Use of randomization to diagnose the impact of observations on analyses and forecasts. Q. J. R. Meteorol. Soc. 131: 2821-2837.
18. Desroziers G, Berre L, Chabot V, Chapnik B. 2009. A posteriori diagnostics in an ensemble of perturbed analyses. Mon. Weather Rev. 137: 3420-3436.
19. Doerenbecher A, Bergot T. 2001. Sensitivity to observations applied to FASTEX cases. Nonlin. Proc. Geophys. 8: 467-481.
20. Fischer C, Montmerle T, Berre L, Auger L, Stefanescu SE. 2005. An overview of the variational assimilation in the ALADIN/France NWP system. Q. J. R. Meteorol. Soc. 131: 3477-3492.
21. Fisher M. 2003. 'Background error covariance modelling'. In Proceedings of Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, ECMWF: Reading, UK. 45-63.
22. Fourrié N, Marchal D, Rabier F, Chapnik B, Desroziers G. 2006. Impact study of the 2003 North Atlantic THORPEX regional campaign. Q. J. R. Meteorol. Soc. 132: 275-295.
23. Girard D. 1989. A fast Monte Carlo cross-validation procedure for large least-squares problems with noisy data. Numer. Math. 56: 1-23.
24. Guidard V, Fourrié N, Brousseau P, Rabier F. 2011. Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes. Q. J. R. Meteorol. Soc. 137: 1975-1987.
25. Kalnay E, Ota Y, Miyoshi T, Liu J. 2012. A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters. Tellus A 64: 18462.
26. Langland RH, Baker NL. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A 56: 189-201.
27. Liu J, Kalnay E. 2008. Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 134: 1327-1335.
28. Ménétrier B, Montmerle T. 2011. Heterogeneous background-error covariances for the analysis and forecast of fog events. Q. J. R. Meteorol. Soc. 137: 2004-2013.
29. Montmerle T, Faccani C. 2009. Mesoscale assimilation of radial velocities from Doppler radars in a preoperational framework. Mon. Weather Rev.137: 1939-1953.
30. Montmerle T, Berre L. 2010. Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale. Q. J. R. Meteorol. Soc. 136: 1408-1420.
31. Parrish D, Derber J, Purser R, Wu W, Pu Z. 1997. The NCEP global analysis system: recent improvement and future plans. J. Meteorol. Soc. Japan 75: 359-365.
32. Raynaud L, Berre L, Desroziers G. 2009. Objective filtering of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 135: 1177-1199.
33. Seity Y, Brousseau P, Malardel S, Hello G, Bénard P, Bouttier F, Lac C, Masson V. 2011. The AROME-France convective-scale operational model. Mon. Weather Rev. 139: 976-991.
34. 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.
35. Wattrelot E, Caumont O, Pradier-Vabre S, Jurasek M, Haase G. 2008. 1D + 3D-Var assimilation of radar reflectivities in the pre-operational AROME model at Météo-France. In Proceedings of Fifth Conference on radar in Meteorology and Hydrology, Helsinki, Finland. Finnish Meteorological Institute. Available at http://erad2008.fmi.fi/proceedings/extended/erad2008-0097-extended.pdf.
36. Yan X, Ducrocq V, Jaubert G, Brousseau P, Poli P, Champollion C, Flamant C, Boniface K. 2009. Benefit of GPS zenith delay assimilation on high-resolution quantitative precipitation forecast of the COPS cases IOP9. Q. J. R. Meteorol. Soc. 135: 1788-1800.