Evaluation of 'GLAMEPS'-a proposed multimodel EPS for short range forecasting

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 63, Issue 3, 2011, Pages 513-530

  Iversen, Trond ^a   Deckmyn, Alex ^b   Santos, Carlos ^c   Sattler, Kai ^d   Bremnes, John Bjørnar ^a   Feddersen, Henrik ^d   Frogner, Inger Lise ^a  

^a NORWEGIAN METEOROLOGICAL INSTITUTE   (Norway)

^b ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM   (Belgium)

^c Terrestrial Remote Sensing Unit   (Spain)

^d DANISH METEOROLOGICAL INSTITUTE   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; CLIMATE PREDICTION; DATA ASSIMILATION; DOWNSCALING; ENSEMBLE FORECASTING; NUMERICAL METHOD; PERTURBATION; SPATIAL RESOLUTION; SYNOPTIC METEOROLOGY; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

EUROPE;

EID: 79954630987     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2010.00507.x     Document Type: Article

References (77)

1. ALADIN International Team. 1997. The ALADIN project: mesoscale modelling seen as a basic tool for weather forecasting and atmospheric research. WMO bull. 46, 317-324.
2. Aspelien, T., Iversen, T., Bremnes, J. B. and Frogner, I.-L. 2011. Short-range probabilistic forecasts from the Norwegian Limited Area EPS. Long-term validation and a polar low study. Tellus 63A, this issue.
3. Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125, 2333-2351.
4. Barkmeijer, J., Iversen, T. and Palmer, T. N. 2003. Forcing singular vectors and other sensitive model structures. Q. J. R. Meteorol. Soc. 129, 2401-2423.
5. Barnston A. G. and Livezey, R. E. 1987. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev. 115, 1083-1126.
6. Bishop, C. H., Etherton, B. J. and Majundar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev. 129, 420-436.
7. Bojarova, J., Gustafsson, N., Johansson, Å. and Vignes, O. 2011. The ETKF rescaling scheme in HIRLAM. Tellus 63A, this issue.
8. Bougeault, P. 1985. A simple parameterization of the large-scale effects of cumulus convection. Mon. Wea. Rev. 113, 2108-2121.
9. Bougeault, P. and Geleyn, J.-F. 1989. Some problems of closure assumption and scale dependency in the parameterization of moist deep convection for numerical weather prediction. Meteorol. Atmos. Phys. 40, 123-135.
10. Bowler, N. E. 2006. Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus 58A, 538-548.
11. Bowler, N. E. 2008. Accounting for the effect of observation errors on verification of MOGREPS. Meteorol. Appl. 15, 199-205.
12. Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS ensemble prediction system. Q. J. R. Meteorol. Soc. 134, 703-722.
13. Bowler, N. E. and Mylne, K. R. 2009. Ensemble transform Kalman filter perturbations for a regional ensemble prediction system. Q. J. R. Meteorol. Soc. 135, 757-766.
14. de Bruijn, C. and van Meijgaard, E. 2005. Verification of HIRLAM with ECMWF physics compared with HIRLAM reference versions. HIRLAM Technical Report No. 63. Available at: hirlam.org.
15. Buizza, R. 1994. Localization of optimal perturbations using a projection operator. Q. J. R. Meteor. Soc. 120, 1647-1682.
16. Buizza, R., Barkmeijer, J., Palmer, T. N. and Richardson, D. 2000. Current status and future developments of the ECMWF ensemble prediction system. Meteorol. Appl. 6, 1-14.
17. Buizza, R., Leutbecher, M. and Isaksen L. 2008. Potential use of analyses in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 134, 2051-2066, doi:.
18. Buizza, R., Miller, M. and Palmer, T. N. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125, 2887-2908.
19. Buizza, R., Tribbia, J., Molteni, F. and Palmer, T. N. 1993. Computation of optimal unstable structures for a numerical weather prediction model. Tellus 45A, 388-407.
20. Calvo, J. 2007. Kain-Fritsch convection in HIRLAM. Present status and prospects. HIRLAM Newslett. 52, 57-64. Available at:
21. Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F. and Rochas, M. 1991. The ARPEGE project at Météo-France. In: Proceedings of the 1991 ECMWF seminar on numerical methods in atmospheric models. Vol. II, 193-231. ECMWF, Shinfield Park, Reading, England
22. Doblas-Reyes, F. J., Hagedorn, R. and Palmer, T. N. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting II. Calibration and combination. Tellus 57A, 234-252.
23. Du, J., DiMego, G., Tracton, M. S. and Zhou, B. 2003. NCEP short-range ensemble forecasting (SREF) system: multi-IC, multi-model and multi-physics approach. Research Activities in Atmospheric and Oceanic Modelling (edited by J. Cote), Report 33, CAS/JSC Working Group Numerical Experimentation (WGNE), WMO/TD-No. 1161, 5.09-5.10. Available at:
24. Du, J., Mullen, S. L. and Sanders, F. 1997. Short-range ensemble forecasting of quantitive precipitation. Mon. Wea. Rev. 125, 2427-2459.
25. Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res. Oceans 99, 10143-10162.
26. Evensen, G. and van Leeuwen, P. 1996. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev. 124, 85-96.
27. Fisher, M. and Courtier, P. 1995. Estimating the covariance matrices of analysis and forecast error in variational data assimilation. ECMWF Tech. Memo. 220. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
28. Fischer, M. and Andersson, E. 2001. Developments in 4D-Var and Kalman filtering. ECMWF Tech. Memo. 347. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
29. Frogner, I.-L., Haakenstad, H. and Iversen, T. 2006. Limited Area ensemble predictions at the Norwegian Meteorological Institute. Q. J. R. Meteorol. Soc. 132, 2785-2808. DOI:
30. Frogner, I.-L. and Iversen, T. 2011. EuroTEPS-a targeted version of ECMWF EPS for the European area. Tellus, 63A, this issue.
31. Garcia-Moya, J. A., Callado, A., Santos, C., Santos, D. and Simarro, J. 2007. Multi-model ensemble for short-range predictability. In: Proceedings of the 3rd International Verification Methods Workshop, ECMWF, Shinfield Park, Reading, RG2 9AX, UK
32. Geleyn, J.-F., Bazile, E., Bougeault, P., Déqué, M., Ivanovici, V. and co-authors 1995. Atmospheric parametrization schemes in Meteo-France's ARPEGE N.W.P. model. In: Proceedings of the 1994 ECMWF Seminar on Physical Parametrizations in Numerical Models, ECMWF, 385-400. Shinfield Park, Reading, England
33. Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M. and co-authors 2001. Three-dimensional variational data assimilation for a limited area model. Part I: general formulation and the background error constraint. Tellus 53A, 425-446.
34. Hagedorn, R., Doblas-Reyes, F. J. and Palmer, T. N. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concepts. Tellus 57A, 219-233.
35. Hágel, E. and Horányi, A. 2007. The ARPEGE/ALADIN limited area ensemble prediction system: the impact of global targeted singular vectors. Meteorologische Zeitschrift 16, 653-663.
36. Hamill, T. M. 2001. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev. 129, 550-560.
37. Hamill, T. M. and Colucci, S. J. 1997. Verification of eta-RSM short-range forecasts. Mon. Wea. Rev. 125, 1312-1327.
38. Hamill, T. M., Mullen, S. L., Snyder, S., Toth, Z. and Braumhefner, D. P. 2000. Ensemble forecasting in the short to medium range. Report from a workshop. Bull. Am. Meteor. Soc. 81, 2653-2664.
39. Hamill, T. M., Whitaker, J. S. and Mullen, S. L. 2006. Reforecasts, an important dataset for improving weather predictions. Bull. Am. Meteorol. Soc. 87, 33-46.
40. Haugen, J. E. and Machenhauer, B. 1993. A spectral Limited Area model formulation with time-dependent boundary conditions applied to the shallow-water equations. Mon. Wea. Rev. 121, 2618-2630.
41. Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796-811.
42. Ivarsson, K. I. 2007. The Rasch Kristjansson large scale condensation. Present status and prospects. HIRLAM Newslett. 52, 50-56. Available at:
43. Johnson, C. and Swinbank, R. 2009. Medium-range multimodel ensemble combination and calibration. Q. J. R. Meteorol. Soc. 135, 777-794.
44. Kain, J. S. 2004. The Kain-Fritsch Convective Parameterization. An update. J. Appl. Meteorol. 43, 170-181.
45. Kain, J. S. and Fritsch, J. M. 1990. A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
46. Kann, A., Wittmann, C., Wang, Y. and Ma, X. 2009. Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution analysis. Mon. Wea. Rev. 137, 3373-3387, doi:.
47. Leith, C. E. 1974. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev. 102, 409-418.
48. Leutbecher, M. 2007. On the representation of initial uncertainties with multiple sets of singular vectors optimized for different criteria. Q. J. R. Meteorol. Soc. 133, 2045-2056, doi:
49. Leutbecher, M. and Palmer, T. N. 2008. Ensemble forecasting. J. Comp. Phys. 227, 3515-3539.
50. Lewis, J. M. 2005. Roots of ensemble forecasting. Mon. Wea. Rev. 133, 1865-1885.
51. Lindskog, M., Gustafsson, N., Navascu'e Ns, B., Mogensen, K. S., Huang, X.-Y. and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part II: observation handling and assimilation experiments. Tellus 53A, 447-468.
52. Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130-141.
53. Lorenz, E. N. 1982. Atmospheric predictability experiments with a large numerical model. Tellus 34, 505-513.
54. Louis, J.-F., Tiedke, M. and Geleyn, J.-F. 1981. A short history of the operational PBL-parameterization at ECMWF. In: Workshop Proceedings on Planetary Boundary Layer Parameterizations, Nov. 1981, pp. 59-79. ECMWF, Shinfield Park, Reading, RG2 9AX, UK
55. Magnusson, L., Nycander, J. and Källén, E. 2009. Flow-dependent versus flow-independent perturbations for ensemble prediction. Tellus 61A, 194-209, doi:.
56. Marsigli, C., Boccanera, F., Montani, A. and Paccagnella, T. 2005. The COSMO-LEPS mesoscale ensemble prediction system: validation of the methodology and verification. Nonl. Proc. Geophys. 12, 527-536.
57. Molteni, F., Buizza, R., Palmer, T. N. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 125, 73-119.
58. Noilhan, J. and S. Planton 1989. A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev. 117, 536-549.
59. Orrell, D., Smith, L., Barkmeijer, J. and Palmer T. N. 2001. Model error in weather forecasting. Nonl. Process. Geophys. 8, 357-371.
60. Osborn, T. J. 2006. Recent variations in the winter North Atlantic Oscillation. Weather 61, 353-355.
61. Palmer, T. N. 2000. Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys. 63, 71-116.
62. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M. 2005. Using bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev. 133, 1155-1174.
63. Rasch, P. J. and Kristjánsson, J. E. 1998. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Clim. 11, 1587-1614.
64. Saetra, Ø., Hersbach, H., Bidlot, J. R. and Richardson, D. S. 2004. Effects of observation errors on the statistics for ensemble spread and reliability. Mon. Wea. Rev. 132, 1487-1501.
65. Sass, B. H., Nielsen, N. W., Jørgensen, J. U. and Amstrup, B. 1999. The Operational HIRLAM System at DMI. DMI Tech Rep. no 99-21. Danmarks Meteorologiske Institutt, Lyngby v. 100, Copenhagen, Denmark
66. Seko, H., Saito, K., Kunii, M., Hara, T., Kyouda, M. and Yamaguchi, M. 2007. Japan area mesoscale ensemble experiments using JMANHM. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling. 37, 5.31-5.32.
67. Simmons, A. and Hollingsworth A. 2002. Some aspects of the improvements in skill of numerical weather prediction. Q. J. R. Meteorol. Soc. 128, 647-677.
68. Stappers, R.J.J. and Barkmeijer, J. 2011. Properties of singular vectors using convective available potential energy as final time norm. Tellus 63A, this issue.
69. Stensrud, D. J., Brooks, H. E., Du, J., Tracton, M. S., Rogers, E. 1999. Using ensembles for short-range forecasting. Mon. Wea. Rev. 127, 433-446.
70. Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 74, 2317-2330.
71. Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297-3319.
72. Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J. and co-authors. 2002: HIRLAM-5 Scientific Documentation HIRLAM-5 Project. Available from SMHI, S-601767 Norrköping, Sweden
73. Wang, X. G. and Bishop, G. H. 2003. A comparison of breeding and ensemble transform Kalman filter forecast schemes. J. Atmos. Sci. 60, 1140-1158.
74. Weigel, A. P. and Bowler, N. E. 2009. Comment on 'Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?'. Q. J. R. Meteorol. Soc. 135, 535-539, doi:
75. Weigel, A. P., Liniger, M. A. and Appenzeller, C. 2008. Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?. Q. J. R. Meteorol. Soc. 134, 241-260.
76. Yang, X. 2007. Status update on operational HIRLAM. HIRLAM Newslett. 52, 114-122. (Can be downloaded from ).
77. Yang, X. 2008. Status of the Reference System. HIRLAM Newslett. 55, 202-203. (Can be downloaded from ).