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References

  • Bannister RN. 2008. A review of forecast-error covariance statistics in variational data assimilation. I: Characteristics and measurements of forecast-error covariances. Q. J. R. Meteorol. Soc. 134: 19511970.
  • Berre L, Pannekoucke O, Desroziers G, Stefanescu S, 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 the workshop on flow-dependent aspects of data assimilation. ECMWF: Reading, UK. 1113.
  • Bouttier F. 1994. A dynamical estimation of forecast error covariances in an assimilation system. Mon. Weather Rev. 122: 23762390.
  • Daley R. 1993. Atmospheric data analysis. Cambridge University Press: Cambridge, UK.
  • Di Giuseppe F, Cesari D, Bonafé G. 2010. Soil initialization strategy for use in mesoscale weather prediction systems. Mon. Weather Rev. submitted.
  • Di Giuseppe F, Elementi M, Cesari D, Paccagnella T. 2009. The potential of variational retrieval of temperature and humidity profiles from Meteosat Second Generation observations. Q. J. R. Meteorol. Soc. 135: 225237.
  • Evensen G. 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53: 343367.
  • Fisher M. 2003. ‘Background-error covariance modelling’. In: Proceedings of seminar on recent developments in data assimilation for atmosphere and ocean. ECMWF: Reading, UK. 4563.
  • Gilbert JC, Lemaréchal C. 1989. Some numerical experiments with variable-storage quasi-Newton algorithms. Math. Programming 45: 407435.
  • Hamill T. 2006. Ensemble-based atmospheric data assimilation. In Predictability of Weather and Climate. Palmer TN, Hagedorn R. (eds.) Cambridge University Press: Cambridge, UK. 124156.
  • Harris BA, Kelly G. 2001. A satellite radiance-bias correction scheme for data assimilation. Q. J. R. Meteorol. Soc. 127: 14531468.
  • Houtekamer P, Mitchell H. 1998. Data assimilation using an Ensemble Kalman Filter technique. Mon. Weather Rev. 126: 796811.
  • Houtekamer P, Lefaivre L, Derome J, Ritchie H, Mitchell H. 1996. A system simulation approach to ensemble prediction. Mon. Weather Rev. 124: 12251242.
  • Joyce R, Janowiak J, Arkin PA, Xie P. 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol. 5: 487503.
  • Kain J, Fritsch J. 1990. A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci. 47: 27842802.
  • Lindskog M, Vignes O, Gustafsson N, Landelius T, Thorsteinsson S. 2007. ‘Background errors in HIRLAM variational data assimilation’. In: Proceedings of Workshop on Flow-dependent Aspects of Data Assimilation, 11–13 June 2007. ECMWF: Reading, UK.
  • Marsigli C, Montani A, Paccagnella T. 2006. ‘The COSMO-SREPS project’. In: Newsletter of the 28th EWGLAM and 13th SRNWP meetings. SRNWP, 912.
  • Montmerle T, Lafore J, Berre L, Fischer C. 2006. Limited-area model error statistics over Western Africa: Comparisons with midlatitude results. Q. J. R. Meteorol. Soc. 132: 213230.
  • Ott E, Hunt B, Szunyogh I, Zimin A, Kostelich E, Corazza M, Kalnay E, Patil D, Yorke J. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A 56: 415428.
  • Pannekoucke O, Berre L, Desroziers G. 2008. Background-error correlation length-scale estimates and their sampling statistics. Q. J. R. Meteorol. Soc. 134: 497508.
  • Parrish DF, Derber JC. 1992. The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Weather Rev. 120: 17471763.
  • Pereira M, Berre L. 2006. The use of an ensemble approach to study the background error covariances in a global NWP model. Mon. Weather Rev. 134: 24662489.
  • Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Ratier A. 2002. An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteorol. Soc. 83: 977992.
  • Široká M, Fischer C, Cassé V, Brožková R, Geleyn J. 2003. The definition of mesoscale selective forecast-error covariances for a limited-area variational analysis. Meteorol. Atmos. Phys. 82: 227244.
  • Steppeler J, Doms G, Schättler U, Bitzer HW, Gassmann A, Damrath U, G G. 2003. Meso-gamma scale forecasts using the non-hydrostatic model LM. Meteorol. Atmos. Phys. 82: 7596.
  • Tiedtke M. 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Weather Rev. 117: 17791800.
  • Zappa M, Rotach M, Arpagaus M, Dorninger M, Hegg C, Montani A, Ranzi R, Ament F, Germann U, Grossi G, Jaun S, Rossa A, Vogt S, Walser A, Wehrhan J, Wunram C. 2008. MAP D-PHASE: Real-time demonstration of hydrological ensemble prediction systems. Atmos. Sci. Lett. 9: 8087.