LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Stewart, L. M.; Dance, Sarah L.; Nichols, Nancy K.; Eyre, J. R.; Cameron, J. (2014)
Publisher: Royal Meteorological Society
Languages: English
Types: Article
Subjects:
Identifiers:doi:10.1002/qj.2211
The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems.\ud \ud The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • of AIRS brightness temperatures: A theoretical study'. Technical Memorandum AC/90. ECMWF: Reading, UK.
    • Collard AD. 2007. Selection of IASI channels for use in Numerical Weather Prediction. Q. J. R. Meteorol. Soc. 133: 1977-1991.
    • Collard AD, McNally AP. 2009. The assimilation of Infrared Atmospheric Sounding Interferometer radiances at ECMWF. Q. J. R. Meteorol. Soc. 135: 1044-1058.
    • Dando ML, Thorpe AJ, Eyre JR. 2007. The optimal density of atmospheric sounder observations in the Met Office NWP sytem. Q. J. R. Meteorol. Soc.
    • Dee DP, Da Silva AM. 1999. Maximum-likelihood estimation of forecast and observation error covariance parameters. Part 1: Methodology. Mon.
    • Weather Rev. 127: 1822-1834.
    • Desroziers G, Berre L, Chapnik B. 2009. 'Objective validation of data assimilation systems: diagnosing sub-optimality'. In Proceedings of seminar on diagnosis of forecasting and data assimilation systems, ECMWF, Reading, UK, 7-10 September 2009. Available from http://www.ecmwf.int/ publications/library/ecpublications/pdf/ seminar/2009/Desroziers.pdf.
    • 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: 3385-3396.
    • Desroziers G, Ivanov S. 2001. Diagnosis and adaptive tuning of observation error parameters in variational assimilation. Q. J. R. Meteorol. Soc. 127: 1433-1452.
    • English SJ, Eyre JR, Smith JA. 1999. A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction. Q. J. R. Meteorol. Soc. 125: 2359-2378.
    • 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: 714-725.
    • Guidard V, Fourrie´ N, Brousseau P, Rabier F. 2011. Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes. Q. J. R. Meteorol. Soc. 137: 1975-1987.
    • Hilton F, Atkinson NC, English SJ, Eyre JR. 2009. Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments. Q. J. R. Meteorol. Soc. 135: 495-505.
    • Hollingsworth A, Lo¨nnberg P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part 1: The wind field.
    • Tellus 38A: 111-136.
    • Joo S, Eyre JR, Marriott R. 2012. 'The impact of Metop and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method'. Forecasting Research Technical Report562. Met Office: Exeter, 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: 3013-3035.
    • Matricardi M, Chevallier F, Kelly G, Thepaut J-N. 2004. An improved general fast radiative transfer model for the assimilation of radiance observations.
    • Q. J. R. Meteorol. Soc. 130: 153-173.
    • Rabier F, Fourrie´ N, Chafai D, Prunet P. 2002. Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances. Q. J. R. Meteorol.
    • Soc. 128: 1011-1027.
    • Rabier F, Bouchard A, Faccani C, Fourrie´ N, Gerard E, Guidard V, Guillaume F, Karbou F, Moll P, Payan C, Poli P, Puech D. 2009.
    • Rawlins F, Ballard SP, Bovis KJ, Clayton AM, Li D, Inverarity GW, Lorenc AC, Payne TJ. 2007. The Met Office global four-dimensional variational data assimilation scheme. Q. J. R. Meteorol. Soc. 133: 347-362.
    • Saunders R, English SJ, Francis P, Rayer P, Brunel P, Kelly G, Bauer P, Salmond D, Dent D. 2005. 'RTTOV-8 the latest update to the RTTOV model'. In Proceedings of International TOVS Study Conference XIV, Beijing, China, 25-31 May 2005. Available from http://cimss.
    • ssec.wisc.edu/itwg/itsc/itsc14/proceedings.
    • Stewart LM. 2010. 'Correlated observation errors in data assimilation'.
    • Stewart LM, Dance SL, English SJ, Eyre JR, Nichols NK. 2009. 'Observation error correlations in IASI radiance data'. Mathematical Report Series 1/2009. University of Reading: UK. Available from http://www.
    • Bormann N, Bauer P. 2010. Estimates of spatial and interchannel observationerror characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data. Q. J. R. Meteorol.
    • Soc. 136: 1036-1050.
    • Bormann N, Collard AD, 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 data. Stewart LM, Dance SL, Nichols NK. 2008. Correlated observation errors in Q. J. R. Meteorol. Soc. 136: 1051-1063. data assimilation. Int. J. Numer. Meth. Fluids 56: 1521-1527.
    • Bouttier F, Kelly G. 2001. Observation-system experiments in the ECMWF Weston P. 2011. 'Progress towards the implementation of correlated 4D-Var data assimilation system. Q. J. R. Meteorol. Soc. 127: 1469-1488. observation errors in 4D-Var'. Forecasting Research Technical Report Collard AD. 2004. 'On the choice of observation errors for the assimilation 560. Met Office: Exeter, UK.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article