Medium-range multimodel ensemble combination and calibration

Quarterly Journal of the Royal Meteorological Society

Volumn 135, Issue 640, 2009, Pages 777-794

  Johnson, Christine ^a   Swinbank, Richard ^a  

^a MET OFFICE   (United Kingdom)

Author keywords

Bias correction; Post processing; Probabilistic; THORPEX; TIGGE; Variance inflation

Indexed keywords

BIAS CORRECTION; POST-PROCESSING; PROBABILISTIC; THORPEX; TIGGE; VARIANCE INFLATION;

WEATHER FORECASTING;

CALIBRATION; CLIMATE PREDICTION; ENSEMBLE FORECASTING; ERROR CORRECTION; ESTIMATION METHOD; LINEARITY; NUMERICAL MODEL; PROBABILITY; SAMPLING BIAS; VARIANCE ANALYSIS; WEATHER FORECASTING;

EID: 69549108852     PISSN: 00359009     EISSN: None     Source Type: Journal    
DOI: 10.1002/qj.383     Document Type: Article

References (42)

1. Berner J, Doblas-Reyes FJ, Palmer TN, Shutts G, Weisheimer A. 2008. Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. Phil. Trans. R. Soc. A: Mathematical, Physical and Engineering Sciences 366: 2561-2579.
2. Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE. 2008. The MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 134: 703-722.
3. Buizza R, Miller M, Palmer TN. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125: 2887-2908.
4. Buizza R, Richardson D, Palmer TN. 2003. Benefits of increased resolution in the ECMWF ensemble system and comparison with poor-mans ensembles. Q. J. R. Meteorol. Soc. 129: 1269-1288.
5. Buizza R, Houtekamer P, Toth Z, Pellerin G, Wei M, Zhu Y. 2005. A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems. Mon. Weather Rev. 133: 1076-1097.
6. Chatfield C. 2004. The analysis of time series. an introduction, 6th edn. Chapman and Hall/CRC Press: Boca Raton, FL, USA.
7. Cui B, Toth Z, Zhu Y, Hou D, Unger D, Beauregard S. 2004. The tradeoff in bias correction between using the latest analysis/modeling system with a short, vs an older system with a long archive, Symposium Proceedings. THORPEX: Montreal. pp. 281-284.
8. Doblas-Reyes FJ, Hagedorn R, Palmer RN. 2005. The rationale behind the success of multimodel ensembles in seasonal forecasting - II. Calibration and combination. Tellus 57A: 234-252.
9. Evans R, Harrison M, Graham RJ, Mylne K. 2000. Joint mediumrange ensembles from the Met. Office and ECMWF systems. Mon. Weather Rev. 128: 3104-3126.
10. Ferro CAT, Richardson DS, Weigel AP. 2008. On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteorol. Appl. 15: 19-24.
11. Hagedorn R, Doblas-Reyes F, Palmer RN. 2005. The rationale behind the success of multimodel ensembles in seasonal forecasting - I. Basic concept. Tellus 57A: 219-233.
12. Hagedorn R, Hamill T, Whitaker J. 2008. Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: 2-meter temperatures. Mon. Weather Rev. 136: 2608-2619.
13. Hamill TM, Juras J. 2006. Measuring forecast skill: is it real skill or is it the varying climatology. Q. J. R. Meteorol. Soc. 132: 2905-2923.
14. Hamill T, Whitaker J, Wei X. 2004. Ensemble reforecasting: improving medium-range forecast skill using retrospective forecasts. Mon. Weather Rev. 132: 1434-1447.
15. Harrison MJ, Palmer TN, Richardson DS, Buizza R. 1999. Analysis and model dependencies in medium-range ensembles: two transplant case studies. Q. J. R. Meteorol. Soc. 125: 2487-2515.
16. Homleid M. 1995. Diurnal corrections of short-term surface temperature forecasts using the Kalman filter. Weather and Forecasting 10: 689-707.
17. Houtekamer PL, Lefaivre L, Derome J, Ritchie H, Mitchell HL. 1996. A system simulation approach to ensemble prediction. Mon. Weather Rev. 124: 1225-1242.
18. Jacks E, Bower JB, Dagostaro VJ, Dallaville JP, Erickson MC, Su JC. 1990. New NGM-based MOS guidance for maxima and minima temperature, probability of precipitation, cloud amount, and surface wind. Weather and Forecasting 5: 128-138.
19. Johnson C. 2006. On the benefits of multimodel ensembles: idealized studies using the Lorenz 1963 model, Technical Report FRTR 492. Met Office: Exeter.
20. Johnson C, Swinbank R. 2008. Medium-range multimodel ensemble combination and calibration, Technical Report 517. Met Office: Exeter.
21. Kendall MG, Stuart A, Ord JK. 1983. The advanced theory of statistics, Vol. 3, 4th edn. Griffin: London.
22. Kharin V, Zwiers F. 2002. Climate predictions with multimodel ensembles. J. Climate 15: 793-799.
23. Krishnamurti T, Kishtawal C, LaRow T, Bachiiiochi DR, Williford C, Gadgil S, Surendran S. 1999. Improved weather and seasonal climate forecasts from multimodel superensemble. Science 13: 4196-4216.
24. Matsueda M, Kyouda M, Tanaka HL, Tsuyuki T. 2007. Daily forecast skill of multi-center grand ensemble. SOLA 3: 29-32.
25. Molteni F, Buizza R, Palmer T, Petroliagia T. 1996. The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 122: 73-119.
26. Mylne K, Evans R, Clark R. 2002. Multi-model multi-analysis ensembles in quasi-operational medium-range forecasting. Q. J. R. Meteorol. Soc. 128: 361-384.
27. Palmer T, Alessandri A, Andersen U, Cantelaube P, Davey M, Delecluse P, Deque M, Diez E, Doblas-Reyes FJ, Feddersen H, Graham R, Gualdi S, Gueremy JF, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres JM, Thomson MC. 2004. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull. Am. Meteorol. Soc. 85: 853-872.
28. Park YY, Buizza R, Leutbecher M. 2008. TIGGE: preliminary results on comparing and combining ensembles. Technical Memo 548. ECMWF: Reading.
29. Pẽna M, van den Dool H. 2008. Consolidation of multi model forecasts by ridge regression: application to Pacific sea surface temperature. J. Climate. 21: 6521-6538. DOI: 10.1175/2008JCLI2226.1.
30. Raftery A, Gneiting T, Balabdaoui F, Polakowski M. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev. 133: 1155-1174.
31. Shutts G, Allen T. 2007. Sub-gridscale parametrization from the perspective of a computer games animator. Atmos. Sci. Lett. 8: 85-92.
32. Stefanova L, Krishnamurti T. 2002. Interpretation of seasonal climate forecast using Brier skill score, the Florida state university superensembles and the AMIP I dataset. J. Climate 15: 537-544.
33. Stephenson D, Coelho C, Doblas-Reyes FJ, Balmaseda M. 2005. Forecast assimilation: a unified framework for the combination of multimodel weather and climate predictions. Tellus 57A: 253-264.
34. Tebaldi C, Knutti R. 2007. The use of the multimodel ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A 365: 2053-2075.
35. Titley H, Savage N, Swinbank R. 2008. Comparison between Met Office and ECMWF medium-range ensemble forecast systems. Technical Report FRTR 512. Met Office: Exeter.
36. Toth Z, Kalnay E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 74: 2317-2330.
37. Weigel AP, Liniger MA, Appenzeller C. 2008. Can multimodel combination really enhance the prediction skill of probabilistic ensemble forecasts? Q. J. R. Meteorol. Soc. 134: 241-260.
38. Wilks D. 2006a. Comparison of ensemble-MOS methods in the Lorenz '96 setting. Meteorol. Appl. 13: 243-256.
39. nd edn. Elsevier: Amsterdam.
40. Wilson LJ, Valĺee M. 2002. The Canadian Updateable Model Output Statistics (UMOS) system: design and development tests. Weather and Forecasting 17: 206-222.
41. Wilson L, Beauregard S, Raftery A, Verret R. 2007. Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging. Mon. Weather Rev. 135: 1364-1385.
42. Woodcock F, Engel C. 2005. Operational consensus forecasts. Weather and Forecasting 20: 101-111.