Skip to main content
Have a personal or library account? Click to login
Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models Cover

Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models

Tellus
Volume 61 (2009): Issue 2
By:  and    
Open Access
|Jan 2009
References

References

  1. Baker , N. and Daley , R. 2000 . Observation and background adjoint sensitivity in the adaptive observation-targeting problem . Quart. J. R. MeteoroL Soc . 126 , 1431 – 1454 .
    Search in Google ScholarBack to article
  2. English , S. , Saunders , R. , Candy , B. , Forsythe , M. and Collard , A. 2004 . Met Office satellite data OSEs. In : Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No . 1228 , 146 – 156 .
    Search in Google ScholarBack to article
  3. Errico , R. M. 2000 . Interpretations of the total energy and rotational energy norms applied to determination of singular vectors . Quart. J. R. MeteoroL Soc . 126 , 1581 – 1599 .
    Search in Google ScholarBack to article
  4. Errico , R. M. 2007 . Interpretation of an adjoint-derived observational impact measure . Tellus 59A , 273 – 276 .
    Search in Google ScholarBack to article
  5. Gelaro , R. , Rosmond , T. E. and Daley , R. 2002 . Singular vector calcu-lations with an analysis error variance metric. Mon. Wea. Re v . 130 , 1166 – 1186 .
    Search in Google ScholarBack to article
  6. Gelaro , R. , Zhu , Y. and Errico , R. M. 2007 . Examination of various-order adjoint-based approximations of observation impact . Meteorologische Zeitschrift 16 , 685 – 692 .
    Search in Google ScholarBack to article
  7. Kelly , G. , McNally , T. , Thepaut , J.-N. and Szyndel , M. 2004 . OSEs of all main data types in the ECMWF operation system . In: Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No . 1228 , 63 – 94 .
    Search in Google ScholarBack to article
  8. Langland , R. H. and Baker , N. 2004 . Estimation of observation im-pact using the NRL atmospheric variational data assimilation adjoint system . Tellus 56A , 189 – 201 .
    Search in Google ScholarBack to article
  9. Le Marshall , J. , Jung , J. , Derber , J. , Chahine , M. , Treadon , R. and co-authors. 2006. Improving global analysis and forecasting with AIRS . Bull. Am. Meteorol. Soc . 87 , 891 - 894 .
    Search in Google ScholarBack to article
  10. Lin , S.-J. 2004 . A vertically Lagrangian finite-volume dynamical core for global models. Mon. Wea. Re v . 132 , 2293 – 2307 .
    Search in Google ScholarBack to article
  11. Lord , S. , Zapotocny , T. and Jung , J. 2004 . Observing system experiments with NCEP’s global forecast system. In : Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No . 1228 , 56 – 62 .
    Search in Google ScholarBack to article
  12. Marseille , G.-J. , Stoffelen , A. and Barkmeijer , J. 2008 . Sensitivity Ob-serving System Experiment (SOSE)-a new effective NWP-based tool in designing the global observing system . Tellus 60A , 216 – 233 .
    Search in Google ScholarBack to article
  13. Orrell , D. , Smith , L. , Barkmeijer , J. and Palmer , T. N. 2001 . Model error in weather forecasting . Nonlinear Processes Geophys . 8 , 357 – 371 .
    Search in Google ScholarBack to article
  14. Rabier , E , Klinker , E. , Courtier , P. and Hollingsworth , A. 1996 . Sen-sitivity of forecast errors to initial conditions . Quart. J. R. MeteoroL Soc . 122 , 121 – 150 .
    Search in Google ScholarBack to article
  15. Reynolds , C. A. and Gelaro , R. 2001 . Remarks on Northern Hemisphere forecast error sensitivity from 1996-2000. Mon. Wea. Re v . 129 , 2145 – 2153 .
    Search in Google ScholarBack to article
  16. Rienecker , M. M. , Suarez , M. J. , Todling , R. , Bacmeister , J. , Takacs , L. and co-authors. 2007. The GEOS-5 Data Assimilation System-documentation of versions 5.0.1 and 5.1.0. NASA TM 104606, Tech-nical Report Series on Global Modeling and Data Assimilation Volume 27 .
    Search in Google ScholarBack to article
  17. Talagrand , O. 1981 . A study of the dynamics of four-dimensional data assimilation . Tellus 33 , 43 – 60 .
    Search in Google ScholarBack to article
  18. Trémolet , Y. 2008 . Computation of observation sensitivity and observa-tion impact in incremental variational data assimilation . Tellus 60A , 964 – 978 .
    Search in Google ScholarBack to article
  19. Wu , W. , Purser , R. J. and Parrish , D. F. 2002 . Three dimensional varia-tional analysis with spatially inhomogeneous covariances. Mon. Wea. Re v . 130 , 2905 – 2916 .
    Search in Google ScholarBack to article
  20. Žagar , N. , Stoffelen , A. , Marseille , G.-J. , Accadia , C. and Schliissel , P. 2008 . Impact assessment of simulated Doppler wind lidars with a multivariate variational assimilation in the tropics. Mon. Wea. Re v . 136 , 2443 – 2460 .
    Search in Google ScholarBack to article
  21. Zhu , Y. and Gelaro , R. 2008 . Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Re v . 136 , 335 – 351 .
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1111/j.1600-0870.2008.00388.x | Journal eISSN: 3035-9554
Journal RSS Feed
Language: English
Page range: 179 - 193
Submitted on: Jul 24, 2008
Accepted on: Dec 2, 2008
Published on: Jan 1, 2009
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2009 Ronald Gelaro, Yanqiu Zhu, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 61 (2009): Issue 2
Previous article
Next article