Comparison of initial perturbation methods for ensemble prediction at convective scale

Quarterly Journal of the Royal Meteorological Society

Volumn 142, Issue 695, 2016, Pages 854-866

  Raynaud, Laure ^a   Bouttier, F ^a  

^a CENTRE NATIONAL DE RECHERCHES MÉTÉOROLOGIQUES   (France)

Author keywords

Background error covariances; Convection permitting models; Ensemble data assimilation; Ensemble prediction; Initial conditions

Indexed keywords

ERRORS; FORECASTING; WEATHER FORECASTING; WIND;

BACKGROUND-ERROR COVARIANCES; ENSEMBLE DATA ASSIMILATION; ENSEMBLE PREDICTION; ENSEMBLE PREDICTION SYSTEMS; HORIZONTAL RESOLUTION; INITIAL CONDITIONS; PRECIPITATION FORECAST; SMALL-SCALE STRUCTURES;

PERTURBATION TECHNIQUES;

CONVECTIVE SYSTEM; COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; WEATHER FORECASTING;

EID: 84947431231     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2686     Document Type: Article

References (33)

1. Berre L. 2000. Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Weather Rev. 128: 644-667.
2. 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 theWorkshop on Flow-Dependent Aspects of Data Assimilation, 11-13 June 2007. 151-168. ECMWF: Reading, UK. http://www.ecmwf.int/publications/library/do/references/list/14092007 (accessed 10 October 2015).
3. Bouttier F, Nuissier O, Vié B, Raynaud L. 2012. Impact of stochastic physics in a convection-permitting ensemble. Mon. Weather Rev. 140: 3706-3721.
4. Bouttier F, Raynaud L, Nuissier O, Ménétrier B. 2015. Sensitivities of the AROME ensemble to initial and surface perturbations during HyMeX. Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2622.
5. Brousseau P, Berre L, Desroziers G, Bouttier F. 2011. Background error covariances for a convective scale data assimilation system: AROME-France 3D-Var. Q. J. R. Meteorol. Soc. 137: 409-422.
6. 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.
7. Clark AJ, Gallus WA, Xue M, Kong F. 2010. Convection-allowing and convection-parameterizing ensemble forecasts of a mesoscale convective vortex and associated severe weather environment. Weather and Forecasting 25: 1052-1081.
8. Courtier P, Freydier C, Geleyn JF, Rabier F, Rochas M. 1991. The ARPEGE project at Météo-France. In Proceedings of the Workshop on Numerical Methods in Atmospheric Models, 9-13 September. 193-231. ECMWF: Reading, UK.
9. Descamps L, Labadie C, Joly A, Bazile E, Arbogast P, Cébron P. 2014. PEARP, the Météo-France short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 141: 1671-1685.
10. Evensen G. 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99: 10143-10162, doi: 10.1029/94JC00572.
11. Fisher M, Courtier P. 1995. Estimating the Covariance Matrices of Analysis and Forecast Error in Variational Data Assimilation, Technical Memorandum 220. ECMWF Reading, UK.
12. Gebhardt C, Theis S, Krahe P, Renner V. 2008. Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett. 9: 67-72.
13. Harnisch F, Keil C. 2015. Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation. Mon. Weather Rev. 143: 1583-1600.
14. Hersbach H. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting 15: 559-570.
15. Hohenegger C, Schär C. 2007. Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci. 64: 4467-4478.
16. Jolliffe IT. 2007. Uncertainty and inference for verification measures. Weather and Forecasting 22: 637-650.
17. Kühnlein C, Keil C, Craig GC, Gebhardt C. 2014. The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation. Q. J. R. Meteorol. Soc. 140: 1552-1562.
18. Lang S, Bonavita M, Leutbecher M. 2015. On the impact of re-centring initial conditions for ensemble forecasts. Q. J. R. Meteorol. Soc. 141: 2571-2581, doi: 10.1002/qj.2543.
19. Magnusson L, Nycander J, Källén E. 2009. Flow-dependent versus flow-independent initial perturbations for ensemble prediction. Tellus 61A: 194-209.
20. Meng Z, Zhang F. 2011. Limited-area ensemble-based data assimilation. Mon. Weather Rev. 139: 2025-2045.
21. Migliorini S, Dixon M, Bannister R, Ballard S. 2011. Ensemble prediction for nowcasting with a convection-permitting model. I:Description of the system and the impact of radar-derived surface precipitation rates. Tellus 63A: 468-496.
22. Nuissier O, Joly B, Vié B, Ducrocq V. 2012. Uncertainty on lateral boundary conditions in a convection-permitting ensemble: A strategy of selection for Mediterranean heavy precipitation events. Nat. Hazards Earth Syst. Sci. 12: 2993-3011.
23. Peralta C, Ben Bouallègue ZB, Theis SE, Gebhardt C, Buchhold M. 2012. Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res. 117: 1984-2012, doi: 10.1029/2011JD016581.
24. Schumacher FS, Clark AJ. 2014. Evaluation of ensemble configurations for the analysis and prediction of heavy-rain-producing mesoscale convective systems. Mon. Weather Rev. 142: 4108-4138.
25. Schwartz CS, Romine GS, Smith KR, Weisman ML. 2014. Characterizing and optimizing precipitation forecats from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Weather and Forecasting 29: 1295-1318, doi: 10.1175/WAF-D-13-00145.1.
26. 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.
27. Szunyogh I, Kalnay E, Toth Z. 1997. A comparison of Lyapunov vectors and optimal vectors in a low resolution GCM. Tellus 49A: 200-227.
28. Tennant W. 2015. Improving initial condition perturbations for MOGREPS-UK. Q. J. R. Meteorol. Soc. 141: 2324-2336, doi: 10.1002/qj.2524.
29. Torn RD, Hakim GJ, Snyder C. 2006. Boundary conditions for limited-area ensemble Kalman filters. Mon. Weather Rev. 134: 2490-2502.
30. Uboldi F, Trevisan A. 2015. Multiple-scale error growth in a convection-resolving model. Nonlin. Processes Geophys. 22: 1-13.
31. Vié B, Nuissier O, Ducrocq V. 2011. Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary coonditions. Mon. Weather Rev. 139: 403-423.
32. Wang Y, Bellus M, Geleyn JF, Ma X, Tian W, Weidle F. 2014. A new method for generating initial condition perturbations in a regional ensemble prediction system: Blending. Mon. Weather Rev. 142: 2043-2059.
33. Zhang F, Snyder C, Rotunno R. 2003. Effects of moist convection on mesoscale predictability. J. Atmos. Sci. 60: 1173-1185.