• Bormann N, Saarinen S, Kelly G, Thépaut J-N. 2003. The spatial structure of observation errors in Atmospheric Motion Vectors from geostationary satellite data. Mon. Weather Rev. 131: 706718.
  • Bormann N, Kobayashi S, Matricardi M, McNally A, Krzeminski B, Thépaut J-N, Bauer P. 2008. ‘Recent developments in the use of ATOVS data at ECMWF’. In Proceedings of the 16th International TOVS Study Conference, Angra dos Reis, Brazil. CIMSS, Univ. Wisonsin: Madison, USA.
  • Bormann N, Salmond D, Matricardi M, Geer A, Hamrud M. 2009.. ‘The RTTOV-9 upgrade for clear-sky radiance assimilation in the IFS’, Technical Memorandum 586, 26pp. ECMWF: Reading, UK. (Available at list/14.).
  • Bormann N, Collard A, Bauer P. 2010. Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS and IASI. Q. J. R. Meteorol. Soc. 136.Submitted. (Paper II.).
  • Chapnik B, Desroziers G, Rabier F, Talagrand O. 2006. Diagnosis and tuning of observational error in a quasi-operational data assimilation setting. Q. J. R. Meteorol. Soc. 132: 543565.
  • Collard A, McNally A. 2009. The assimilation of Infrared Atmospheric Sounding Interferometer radiances at ECMWF. Q. J. R. Meteorol. Soc. 135: 10441058.
  • Dando M, Thorpe A, Eyre J. 2007. The optimal density of atmospheric sounder observations in the Met Office NWP system. Q. J. R. Meteorol. Soc. 133: 19331943.
  • Dee D. 2004. ‘Variational bias correction of radiance data in the ECMWF system’. In ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP. ECMWF: Reading, UK; pp 97112.
  • Dee D, da Silva A. 1999. Maximum-likelihood estimation of forecast and observation error covariance parameters. Part I: Methodology. Mon. Weather Rev. 127: 18221834.
  • Desroziers G, Ivanov S. 2001. Diagnosis and adaptive tuning of information error parameters in a variational assimilation. Q. J. R. Meteorol. Soc. 127: 14331452.
  • Desroziers G, Berre L, Chapnik B, Poli P. 2005. Diagnosis of observation, background and analysis-error statistics in observation space. Q. J. R. Meteorol. Soc. 131: 33853396.
  • Desroziers G, Berre L, Chapnik B. 2009. ‘Objective validation of data assimilation systems: Diagnosing sub-optimality’. In Proceedings of the ECMWF workshop on diagnostics of data assimilation system performance. ECMWF: Reading, UK. In press.
  • Fisher M. 2003. Background error covariance modelling. In Proceedings of the ECMWF Seminar on Recent Developments in Data Assimilation from Atmosphere and Ocean. ECMWF: Reading, UK; pp 4564.
  • Garand L, Heilliette S, Buehner M. 2007. Interchannel error correlation associated with AIRS radiance observations: Inference and impact in data assimilation. J. Appl. Meteorol. 46: 714725.
  • Goodrum G, Kidwell K, Winston W. 2009. ‘NOAA KLM User’s Guide with NOAA-N, -N-Prime supplement'. NOAA. Available at
  • Hollingsworth A, Lönnberg P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus 38A: 111136.
  • Krzeminski B, Bormann N, Kelly G, McNally T, Bauer P. 2009. ‘Revision of the HIRS cloud detection’. EUMETSAT/ECMWF Fellowship Programme Research Report 19, 15pp. ECMWF: Reading, UK.
  • Liu Z-Q, Rabier F. 2003. The potential of high-density observations for numerical weather prediction: A study with simulated observations. Q. J. R. Meteorol. Soc. 129: 30133035.
  • Ménard R, Yang Y, Rochon Y. 2009. ‘Convergence and stability of estimated error variances derived from assimilation residuals in observation space’. In Proceedings of the ECMWF Workshop on Diagnostics of Data Assimilation System Performance. ECMWF: Reading, UK. In press.
  • Rutherford ID. 1972. Data assimilation by statistical interpolation of forecast error fields. J. Atmos. Sci. 29: 809815.
  • Saunders R, Matricardi M, Brunel P. 1999. An improved fast radiative transfer model for assimilation of satellite radiance observations. Q. J. R. Meteorol. Soc. 125: 14071426.
  • Sherlock V. 2000. ‘Impact of RTIASI fast radiative transfer model error on IASI retrieval accuracy’. Forecasting Research Technical Report 319, 34pp. Met. Office: Bracknell, UK.
  • Stark J, Donlon C, Martin M, McCulloch M. 2007. ‘OSTIA: An operational, high resolution, real time, global sea surface temperature analysis system’. In Proceedings of the Oceans '07 Conference, Aberdeen, UK. IEEE/OES, 061214–029.
  • Stewart L, Cameron J, Dance S, English S, Eyre J, Nichols N. 2009. ‘Observation error correlations in IASI radiance data’. Mathematics Report Series 1/2009, 26pp. Univ. Reading: Reading, UK. (Available from