Skip to main content
Have a personal or library account? Click to login
Using lagged covariances in data assimilation Cover

Using lagged covariances in data assimilation

By:  and    
Open Access
|Jan 2017

References

  1. Balmaseda , M. A. , Mogensen , K. and Weaver , A. T. 2013 . Evaluation of the ECMWF ocean reanalysis system ORAS4 . Q. J. Roy. Meteor. Soc. 139 ( 674 ), 1132 1161 . DOI: 10.1002/qj.2063 .
  2. Bishop , C. H. , Etherton , B. J. and Majumdar , S. J. 2001 . Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects . Mon. Weather Rev. 129 ( 3 ), 420 436 . DOI: 10.1175/1520-0493(2001)129\lt0420:ASWTET\gt2.0.CO;2 .
  3. Bloom , S. C. , Takacs , L. L. , da Silva , A. M. and Ledvina , D. 1996 . Data assimilation using incremental analysis updates . Mon. Weather Rev. 124 ( 6 ), 1256 1271 . DOI: 10.1175/1520-0493(1996)124\lt1256:DAUIAU\gt2.0.CO;2 .
  4. Bretherton , C. S. , Smith , C. and Wallace , J. M. 1992 . An intercomparison of methods for finding coupled patterns in climate data . J. Climate 5 ( 6 ), 541 560 . DOI: 10.1175/1520-0442(1992)005\lt0541:AIOMFF\gt2.0.CO;2 .
  5. Brockwell , P. J. and Davis , R. A. 2002 . Introduction to Time Series and Forecasting . Springer-Verlag , New York , pp. 137 178 .
  6. Evensen , G. and van Leeuwen , P. J. 2000 . An ensemble Kalman smoother for nonlinear dynamics . Mon. Weather Rev. 128 ( 6 ), 1852 1867 . DOI: 10.1175/1520-0493(2000)128\lt1852:AEKSFN\gt2.0.CO;2 .
  7. Fairbairn , D. , Pring , S. R. , Lorenc , A. C. and Roulstone , I. 2014 . A comparison of 4DVar with ensemble data assimilation methods . Q. J. Roy. Meteor. Soc. 140 ( 678 ), 281 294 . DOI: 10.1002/qj.2135 .
  8. Fisher , M. , Leutbecher , M. and Kelly , G. A. 2005 . On the equivalence between Kalman smoothing and weak-constraint four-dimensional variational data assimilation . Q. J. Roy. Meteor. Soc. 131 ( 613 ), 3235 3246 . DOI: 10.1256/qj.04.142 .
  9. Goodliff , M. , Amezcua , J. and van Leeuwen , P. J. 2015 . Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity . Tellus A 67 , 26928 . DOI: 10.3402/tellusa.v67.26928 .
  10. Jackson , L. C. , Peterson , K. A. , Roberts , C. D. and Wood , R. A. 2016 . Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening . Nat. Geosci. 9 ( 7 ), 518 522 . DOI: 10.1038/ngeo2715 .
  11. Köhl , A. 2015 . Evaluation of the GECCO2 ocean synthesis: transports of volume, heat and freshwater in the Atlantic . Q. J. Roy. Meteor. Soc. 141 ( 686 ), 166 181 . DOI: 10.1002/qj.2347 .
  12. LeVeque , R. J. 1992 . Numerical Methods for Conservation Laws Birkhäuser , Basel , pp. 97 113 .
  13. Lorenc , A. C. and Payne , T. 2007 . 4D-Var and the butterfly effect: statistical four-dimensional data assimilation for a wide range of scales . Q. J. Roy. Meteor. Soc. 133 ( 624 ), 607 614 . DOI: 10.1002/qj.36 .
  14. Lorenz , E. N. and Emanuel , K. A. 1998 . Optimal sites for supplementary weather observations: simulation with a small model . J. Atmos. Sci. 55 ( 3 ), 399 414 . DOI: 10.1175/1520-0469(1998)055\lt0399:OSFSWO\gt2.0.CO;2 .
  15. McCullagh , P. and Nelder , J. A. 1989 . Generalized Linear Models Chapman & Hall , London , pp. 48 97 .
  16. Ménard , R. and Daley , R. 1996 . The application of Kalman smoother theory to the estimation of 4DVAR error statistics . Tellus A 48 ( 2 ), 221 237 . DOI: 10.1034/j.1600-0870.1996.t01-1-00003.x .
  17. Ollinaho , P. , Lock , S.-J. , Leutbecher , M. , Bechtold , P. , Beljaars , A. and co-authors. 2016 . Towards process-level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble . Q. J. Roy. Meteor. Soc. 143 ( 702 ), 408 422 . DOI: 10.1002/qj.2931 .
  18. Pires , C. , Vautard , R. and Talagrand , O. 1996 . On extending the limits of variational assimilation in nonlinear chaotic systems . Tellus A 48 ( 1 ), 96 121 . DOI: 10.3402/tellusa.v48i1.11634 .
  19. Polo , I. , Robson , J. , Sutton , R. and Balmaseda , M. A. 2014 . The importance of wind and buoyancy forcing for the boundary density variations and the geostrophic component of the AMOC at 26°N . J. Phys. Oceanogr. 44 ( 9 ), 2387 2408 . DOI: 10.1175/JPO-D-13-0264.1 .
  20. Sugiura , N. , Awaji , T. , Masuda , S. , Mochizuki , T. , Toyoda , T. , Miyama , T. and co-authors. 2008 . Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations . J. Geophys. Res. Oceans 113 ( C10 ), C10017 . DOI: 10.1029/2008JC004741 .
  21. Swanson , K. , Vautard , R. and Pires , C. 1998 . Four-dimensional variational assimilation and predictability in a quasi-geostrophic model . Tellus A 50 ( 4 ), 369 390 . DOI: 10.1034/j.1600-0870.1998.t01-4-00001.x .
  22. Talagrand , O. and Courtier , P. 1987 . Variational assimilation of meteorological observations with the adjoint vorticity equation I: theory . Q. J. Roy. Meteor. Soc. 113 ( 478 ), 1311 1328 . DOI: 10.1002/qj.49711347812 .
  23. Waters , J. , Lea , D. J. , Martin , M. J. , Mirouze , I. , Weaver , A. and co-authors. 2015 . Implementing a variational data assimilation system in an operational 1/4 degree global ocean model . Q. J. Roy. Meteor. Soc. 141 ( 687 ), 333 349 . DOI: 10.1002/qj.2388 .
Language: English
Page range: 1377589 - 1377589
Submitted on: Jan 6, 2017
Accepted on: Aug 30, 2017
Published on: Jan 1, 2017
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2017 C. M. Thomas, K. Haines, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.