Ensemble prediction for nowcasting with a convection-permitting model-I: Description of the system and the impact of radar-derived surface precipitation rates

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 63, Issue 3, 2011, Pages 468-496

  Migliorini, Stefano ^a   Dixon, Mark ^b   Bannister, Ross ^a   Ballard, Sue ^b  

^a UNIVERSITY OF READING   (United Kingdom)

^b MET OFFICE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AIR TEMPERATURE; ATMOSPHERIC CONVECTION; CLIMATE PREDICTION; CONVECTIVE SYSTEM; ENSEMBLE FORECASTING; HISTOGRAM; KALMAN FILTER; NOWCASTING; NUMERICAL MODEL; PRECIPITATION INTENSITY; RADAR; RELATIVE HUMIDITY; WIND VELOCITY;

EID: 79954558836     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2010.00503.x     Document Type: Article

References (32)

1. Bannister, R., Migliorini, S. and Dixon, M. 2011. Ensemble prediction with a convection-permitting model for nowcasting. Part II: Forecast error statistics. Tellus 63A, this issue.
2. Bloom, S., Takacs, L., da Silva, A. and Ledvina, D. 1996. Data assimilation using incremental analysis updates. Mon. Wea. Rev. 124, 1256-1271.
3. Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc. 134, 703-722.
4. Clark, P. 2009. Limited area modelling in CRM mode. In: Proceedings of the ECMWF Seminar on Parametrization of Subgrid Physical Processes. ECMWF, 165-192. Available at:
5. Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., and co-authors. 2005. A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Quart. J. Roy. Meteor. Soc. 131, 1759-1782.
6. Dixon, M., Li, Z., Lean, H., Roberts, N. and Ballard, S. 2009. Impact of data assimilation on forecasting convection over the United Kingdom using a high-resolution version of the Met Office Unified Model. Mon. Wea. Rev. 137, 1562-1584.
7. Done, J. M., Craig, G. C., Gray, S. L., Clark, P. A. and Gray, M. E. B. 2006. Mesoscale simulations of organized convection: importance of convective equilibrium. Quart. J. Roy. Meteor. Soc. 132, 737-756.
8. Fritsch, J. M. and Carbone, R. R. 2004. Improving quantitative precipitation forecasts in the warm season: a USWRP research and development strategy. Bull. Amer. Meteor. Soc. 85, 955-965.
9. Gilmore, M. S., Straka, J. M. and Rasmussen, E. N. 2004. Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev. 132, 2610-2627.
10. Golding, B. W. 1998. Nimrod: a system for generating automated very short range forecasts. Meteor. Appl. 5, 1-16.
11. Hamill, T. M. 2001. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev. 129, 550-560.
12. Harrison, D. L., Driscoll, S. J. and Kitchen, M. 2000. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteor. Appl. 7, 135-144.
13. Hohenegger, C. and Schär, C. 2007. Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci. 64, 4467-4478.
14. Jones, C. D. and Macpherson, B. 1997. A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model. Meteor. Appl. 4, 269-277.
15. Koizumi, K., Ishikawa, Y. and Tsuyuki, T. 2005. Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts. SOLA 1, 45-48.
16. Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A. and co-authors. 2008. The Met. Office global three-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc. 131, 1759-1782.
17. Lilly, D. K. 1990. Numerical prediction of thunderstorms-has its time come? Quart. J. Roy. Meteor. Soc. 116, 779-798.
18. Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby, N. B., Andrews, P. L. F. and co-authors. 2000. Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Wea. Rev. 136, 3408-3424.
19. Macpherson, B., Wright, B. J., Hand, W. H. and Maycock, A. J. 1996. The impact of MOPS moisture data in the U.K. Meteorological Office Mesoscale Data Assimilation Scheme. Mon. Wea. Rev. 124, 1746-1766.
20. Mass, C. F., Ovens, D., Westrick, K. and Colle, B. A. 2002. Does increasing horizontal resolution produce more skilful forecasts? Bull. Amer. Meteor. Soc. 83, 407-430.
21. Pagé, C., Fillion, L. and Zwack, P. 2007. Diagnosing summertime mesoscale vertical motion: implications for atmospheric data assimilation. Mon. Wea. Rev. 135, 2076-2094.
22. Park, S. K. and Droegemeier, K. K. 2000. Sensitivity analysis of a 3D convective storm: implications for variational data assimilation and forecast error. Mon. Wea. Rev. 128, 140-159.
23. Pires, C. A., Talagrand, O. and Bocquet, M 2010. Diagnosis and impacts of non-Gaussianity of innovations in data assimilation. Phys. D: Nonlinear Phenom. 239, 1701-1717.
24. Sakov, P. and Oke, P. R. 2008. Implications of the form of the ensemble transformation in the ensemble square root filters. Mon. Wea. Rev. 136, 1042-1053.
25. Stensrud, D. J., Xue, M., Wicker, L. J., Kelleher, K. E., Foster, M. P. and co-authors. 2009. Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc. 90, 1487-1499.
26. Sun, J. 2005. Convective-scale assimilation of radar data: progress and challenges. Quart. J. Roy. Meteor. Soc. 131, 3439-3463.
27. Talagrand, O., Vautard, R. and Strauss, B. 1997. Evaluation of probabilistic prediction systems. In: Proceedings, ECMWF Workshop on Predictability, Shinfield Park, Reading, UK, 1-25.
28. Tong, M. and Xue, M. 2008. Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble Kalman filter. Part I: Sensitivity analysis and parameter identifiability. Mon. Wea. Rev. 136, 1630-1648.
29. Wang, X. and Bishop, C. H. 2003. A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci 60, 1140-1158.
30. Wang, X., Bishop, C. H. and Julier, S. J. 2004. Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble?. Mon. Wea. Rev. 132, 1590-1605.
31. Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences, 2nd edition, Volume 59. International Geophysics Series. Academic Press.
32. Wilson, D. R. and Ballard, S. P. 1999. A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quart. J. Roy. Meteor. Soc. 131, 1607-1636.