SEARCH

SEARCH BY CITATION

References

  • Baker NL, Langland RH. 2009. Diagnostics for evaluating the impact of satellite observations. In Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Park SK, Xu L. (eds.) JCSDA: Camp Springs, Maryland, USA. 177196.
  • Baker NL, Daley R. 2000. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q. J. R. Meteorol. Soc. 126: 14311454.
  • Bannister RN. 2008a. A review of forecast-error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast-error covariances. Q. J. R. Meteorol. Soc. 134: 19511970.
  • Bannister RN. 2008b. A review of forecast-error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast-error covariance statistics. Q. J. R. Meteorol. Soc. 134: 19711996.
  • Bergot T, Doerenbecher A. 2002. A study on the optimization of the deployment of targeted observations using adjoint-based methods. Q. J. R. Meteorol. Soc. 128: 16891712.
  • Buehner M, Gauthier P, Liu Z. 2005. Evaluation of new estimates of background- and observation-error covariances for variational assimilation. Q. J. R. Meteorol. Soc. 131: 33733383.
  • Cacuci DG. 2003. Sensitivity and Uncertainty Analysis, Vol. 1: Theory. Chapman & Hall/CRC Press: London and New York.
  • Cardinali C. 2009. Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc. 135: 239250.
  • Chapnik B, Desroziers G, Rabier F, Talagrand O. 2004. Properties and first applications of an error statistics tuning method in variational assimilation. Q. J. R. Meteorol. Soc. 130: 22532275.
  • 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.
  • Cohn SE. 1997. An introduction to estimation theory. J. Meteorol. Soc. Japan 75: 257288.
  • Courtier P, Thépaut JN, Hollingsworth A. 1994. A strategy of operational implementation of 4D-Var using an incremental approach. Q. J. R. Meteorol. Soc. 120: 13671388.
  • Daescu DN. 2008. On the sensitivity equations of four-dimensional variational (4D-Var) data assimilation. Mon. Weather Rev. 136: 30503065.
  • Daescu DN. 2009. On the deterministic observation impact guidance: A geometrical perspective. Mon. Weather Rev. 137: 35673574.
  • Daescu DN, Todling R. 2009. Adjoint estimation of the variation in model functional output due to the assimilation of data. Mon. Weather Rev. 137: 17051716.
  • Daley R. 1991. Atmospheric Data Analysis. Cambridge University Press: Cambridge, UK.
  • Daley R, Barker E. 2001. NAVDAS: Formulation and diagnostics. Mon. Weather Rev. 129: 869883.
  • Dee DP. 1995. On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Weather Rev. 123: 11281145.
  • Dee DP, Da Silva AM. 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 observation-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, Chabot V, Chapnik B. 2009. Aposteriori diagnostics in an ensemble of perturbed analyses. Mon. Weather Rev. 137: 34203436.
  • Doerenbecher A, Bergot T. 2001. Sensitivity to observations applied to FASTEX cases. Nonlinear Proc. Geophys. 8: 467481.
  • Fourrié N, Doerenbecher A, Bergot T, Joly A. 2002. Adjoint sensitivity of the forecast to TOVS observations. Q. J. R. Meteorol. Soc. 128: 27592777.
  • Frehlich R. 2006. Adaptive data assimilation including the effect of spatial variations in observation error. Q. J. R. Meteorol. Soc. 132: 12251257.
  • Gaspari G, Cohn SE. 1999. Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc. 125: 723757.
  • Gelaro R, Zhu Y, Errico RM. 2007. Examination of various-order adjoint-based approximations of observation impact. Meteorol. Zeitschrift 16: 685692.
  • Gelaro R, Zhu Y. 2009. Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models. Tellus 61A: 179193.
  • Giering R, Kaminski T, Todling R, Errico RM, Gelaro R, Winslow N. 2005. Generating tangent linear and adjoint versions of NASA/GMAO's Fortran-90 global weather forecast model. In Automatic Differentiation: Applications, Theory, and Implementations. Vol. 50 of Lecture Notes in Computational Science and Engineering. Bücker HM, Corliss G, Hovland P, Naumann U, Norris B. (eds.) 275284. Springer: New York.
  • Hamill TM, Snyder C. 2002. Using improved background-error covariances from an ensemble Kalman filter for adaptive observations. Mon. Weather Rev. 130: 15521572.
  • Janjić T, Cohn SE. 2006. Treatment of observation error due to unresolved scales in atmospheric data assimilation. Mon. Weather Rev. 134: 29002915.
  • Jazwinski AH. 1970. Stochastic Processes and Filtering Theory. Academic Press.
  • Joiner J, Brin E, Treadon R, Derber J, Van Delst P, Da Silva A, Le Marshall J, Poli P, Atlas R, Bungato D, Cruz C. 2007. Effects of data selection and error specification on the assimilation of AIRS data. Q. J. R. Meteorol. Soc. 133: 181196.
  • Kalnay E. 2002. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press: Cambridge, UK.
  • Langland RH. 2005. Observation impact during the North Atlantic TReC-2003. Mon. Weather Rev. 133: 22972309.
  • Langland RH, Baker NL. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus 56A: 189201.
  • Le Dimet F-X, Ngodock H-E, Luong B, Verron J. 1997. Sensitivity analysis in variational data assimilation. J. Meteorol. Soc. Japan 75: 245255.
  • Le Marshall J, Jung J, Derber J, Chahine M, Treadon R, Lord SJ, Goldberg M, Wolf W, Liu HC, Joiner J, Woollen J, Todling R, van Delst P, Tahara Y. 2006. Improving global analysis and forecasting with AIRS. Bull. Amer. Meteorol. Soc. 87: 891894.
  • Li H, Kalnay E, Miyoshi T. 2009. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 135: 523533.
  • Lin SJ. 2004. A vertically Lagrangian finite-volume dynamical core for general circulation models. Mon. Weather Rev. 132: 22932307.
  • Liu J, Kalnay E. 2008. Estimation of observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 134: 13271335.
  • Lorenc AC. 2003. Modelling of error covariances by 4D-Var data assimilation. Q. J. R. Meteorol. Soc. 129: 31673182.
  • Lorenz EN, Emanuel KA. 1998. Optimal sites for supplementary weather observations: Simulation with a small model. J. Atmos. Sci. 55: 399414.
  • Magnus JR, Neudecker H. 1999. Matrix Differential Calculus with Applications in Statistics and Econometrics. (Revised edition). John Wiley & Sons Ltd: New York and Chichester, UK.
  • Ménard R, Daley R. 1996. The application of the Kalman smoother theory to the estimation of the 4DVAR error statistics. Tellus 48A: 221237.
  • Rienecker MM, Suarez MJ, Todling R, Bacmeister J, Takacs L, Liu H-C, Gu W, Sienkiewicz M, Koster RD, Gelaro R, Stajner I, Nielsen JE. 2008. The GEOS-5 Data Assimilation SystemDocumentation ofversions 5.0.1, 5.1.0, and 5.2.0. NASA/TM-2008-104606, Vol. 27, Tech. Rep. Series on Global Modeling and Assimilation. NASA Goddard Space Flight Center: Greenbelt, Maryland, USA.
  • Rosmond T, Xu L. 2006. Development of NAVDAS-AR: Non-linear formulation and outer loop tests. Tellus 58A: 4558.
  • Talagrand O. 1999. ‘A posteriori verification of analysis and assimilation algorithms’. In Proceedings of workshop on Diagnosis of Data Assimilation Systems, 2–4 November 1998, 1728. ECMWF: Reading, UK.
  • Talagrand O. 2003. ‘Objective validation and evaluation of data assimilation’ In Proceedings of seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, 8–12 September 2003, 287299. ECMWF: Reading, UK.
  • Torn RD, Hakim GJ. 2008. Ensemble-based sensitivity analysis. Mon. Weather Rev. 136: 663677.
  • Trémolet Y. 2007a. Incremental 4D-Var convergence study. Tellus 59A: 706718.
  • Trémolet Y. 2007b. First-order and higher-order approximations of observation impact. Meteorol. Zeitschrift 16: 693694.
  • Trémolet Y. 2008. Computation of observation sensitivity and observation impact in incremental variational data assimilation. Tellus 60A: 964978.
  • Wu W, Purser RJ, Parrish DF. 2002. Three-dimensional variational analysis with spacially inhomogeneous covariances. Mon. Weather Rev. 130: 29052916.
  • Zhu Y, Gelaro R. 2008. Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Weather Rev. 136: 335351.