Skip to main content
Have a personal or library account? Click to login
To what extent is your data assimilation scheme designed to find the posterior mean, the posterior mode or something else? Cover

To what extent is your data assimilation scheme designed to find the posterior mean, the posterior mode or something else?

Open Access
|Dec 2016

References

  1. Bennett A. F . Inverse modeling of the ocean and atmosphere. 2002; Cambridge University Press, Cambridge, United Kingdom. 256.
  2. Bishop C. H. , Hodyss D . Adaptive ensemble covariance localization in ensemble 4D-Var state estimation. Mon. Weather Rev. 2011; 139: 12411255.
  3. Bocquet M. , Sakov P . An iterative ensemble Kalman smoother. Q. J. Roy. Meteorol. Soc. 2014; 140: 15211535.
  4. Buehner M. , Houtekamer P. L. , Charette C. , Mitchell H. L. , He B . Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: description and single-observation experiments. Mon. Weather Rev. 2009; 138: 15501566.
  5. Buehner M. , Morneau J. , Charette C . Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlin. Processes Geophys. 2013; 20: 669682.
  6. Clayton A. M. , Lorenc A. C. , Barker D. M . Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Q. J. Roy. Meteorol. Soc. 2013; 139: 14451461.
  7. Courtier P . Dual formulation of four-dimensional variational assimilation. Q. J. Roy. Meteorol. Soc. 1997; 123: 24492461.
  8. Courtier P. , Thépaut J.-N. , Hollingsworth A . A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. Roy. Meteorol. Soc. 1994; 120: 13671387.
  9. El Akkraoui A. , Gauthier P. , Pellerin S. , Buis S . Intercomparison of the primal and dual formulations of variational data assimilation. Q. J. Roy. Meteorol. Soc. 2008; 134: 10151025.
  10. Errico R. M. , Raeder K . An examination of the accuracy of the linearization of a mesoscale model with moist physics. Q. J. Roy. Meteorol. Soc. 1999; 125: 169195.
  11. Errico R. M. , Vukicevic T. , Reader K . Examination of the accuracy of a tangent linear model. Tellus A. 1993; 45: 462477.
  12. Fairbairn D. , Pring S. R. , Lorenc A. C. , Roulstone I . A comparison of 4DVar with ensemble data assimilation methods. Q. J. Roy. Meteorol. Soc. 2014; 140: 281294.
  13. Gauthier P . Chaos and quadri-dimensional data assimilation: a study based on the Lorenz model. Tellus. 1992; 44A: 217.
  14. Jazwinski A. H . Stochastic Processes and Filtering Theory. 1970; Inc., Mineola, New York: Dover Publications. 376.
  15. Kleist D. T. , Ide K . An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Weather Rev. 2015; 143: 452470.
  16. Klinker E. , Rabier F. , Kelly G. , Mahfouf J.-F . The ECMWF operational implementation of four dimensional variational data assimilation. Experimental results and diagnostics with operational configuration. Q. J. Roy. Meteorol. Soc. 2000; 126: 11911215.
  17. Kuhl D. D. , Rosmond T. E. , Bishop C. H. , McLay J. , Baker N. L . Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework. Mon. Weather Rev. 2013; 141: 27402758.
  18. Le Dimet F.-X. , Talagrand O . Variational algorithms for analysis and assimilation of meteorological observations. Tellus A. 1986; 38: 97110.
  19. Lewis J. M. , Lakshmivarahan S. , Dhall S. K . Dynamic data assimilation: a least squares approach. 2006; , Cambridge: Cambridge University Press. 654.
  20. Li Z. , Navon I. M . Optimality of variational data assimilation and its relationship with the Kalman filter and smoother. Q. J. Roy. Meteorol. Soc. 2001; 127: 661683.
  21. Lorenc A. C . Analysis methods for numerical weather prediction. Q. J. Roy. Meteorol. Soc. 1986; 112: 11771194.
  22. Lorenc A. C . Development of an operational variational assimilation scheme. J. Meteorol. Soc. Jpn. 1997; 75: 339346.
  23. Lorenc A. C . Modelling of error covariances by 4D-Var data assimilation, Q. J. Roy. Meteorol. Soc. 2003a; 129: 31673182.
  24. Lorenc A. C . The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var. Q. J. Roy. Meteorol. Soc. 2003b; 129: 31833203.
  25. Lorenc A. C. , Payne T . 4D-Var and the butterfly effect: statistical four-dimensional data assimilation for a wide range of scales. Q. J. Roy. Meteorol. Soc. 2007; 133: 607614.
  26. Lorenc A. C. , Bowler N. E. , Clayton A. M. , Pring S. R. , Fairbairn D . Comparison of hybrid-4DEnVar and Hybrid-4DVar data assimilation methods for global NWP. Mon. Weather Rev. 2015; 143: 212229.
  27. Mahfouf J.-F. , Rabier F . The ECMWF operational implementation of four dimensional variational data assimilation. Experimental results with improved physics. Q. J. Roy. Meteorol. Soc. 2000; 126: 11711190.
  28. Navon I. M. , Zou X. , Derber J. , Sela J . Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Weather Rev. 1992; 120: 14331446.
  29. Payne T. J . The linearization of maps in data assimilation. Tellus A. 2013; 65: 18840.
  30. Pedlosky J . Geophysical Fluid Dynamics. 1987; Springer, New York. 710.
  31. Rabier F. , Järvinen H. , Klinker E. , Mahfouf J.-F. , Simmons A . The ECMWF operational implementation of four-dimensional variational assimilation. I: experimental results with simplified physics. Q. J. Roy. Meteorol. Soc. 2000; 126: 11431170.
  32. Rabier F . Overview of global data assimilation developments in numerical weather-prediction centres. Q. J. Roy. Meteorol. Soc. 2005; 131: 32153233.
  33. Rawlins F. , Ballard S. P. , Bovis K. J. , Clayton A. M. , Li D. , co-authors . The Met Office global four-dimensional variational data assimilation scheme. Q. J. Roy. Meteorol. Soc. 2007; 133: 347362.
  34. Rosmond T. , Xu L . Development of NAVDAS-AR: non-linear formulation and outer loop tests. Tellus A. 2006; 58: 4558.
  35. Sakov P. , Oliver D. S. , Bertino L . An iterative EnKF for strongly nonlinear systems. Mon. Weather Rev. 2012; 140: 19882004.
  36. Talagrand O. , Courtier P . Variational assimilation of meteorological observations with the adjoint vorticity equation. I: theory. Q. J. Roy. Meteorol. Soc. 1987; 113: 13111328.
  37. Tarantola A . Inverse problem theory and methods for model parameter estimation. 2005; , Philadelphia: Society for Industrial and Applied Mathematics. 342.
  38. Tremolet Y . Diagnostics of linear and incremental approximations in 4D-Var, Q. J. Roy. Meteorol. Soc. 2004; 130: 22332251.
  39. Wang X. , Lei T . GSI-based four-dimensional ensemble-variational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP global forecast system. Mon. Wea. Rev. 2014; 142: 33033325.
  40. Zhang X. , Huang X.-Y. , Liu J. , Poterjoy J. , Weng Y. , co-authors . Development of an efficient regional four-dimensional variational data assimilation system for WRF. J. Atmos. Oceanic Technol. 2014; 31: 27772794.
Language: English
Page range: 30625 - 30625
Submitted on: Dec 3, 2015
Accepted on: Sep 6, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2016 Daniel Hodyss, Craig H. Bishop, Matthias Morzfeld, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.