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Gaussian approximations in filters and smoothers for data assimilation Cover

Gaussian approximations in filters and smoothers for data assimilation

Open Access
|Jan 2019

References

  1. Anderson, J. L. 2010. A non-gaussian ensemble filter update for data assimilation. Mon. Wea. Rev. 138, 41864198. doi:10.1175/2010MWR3253.1
  2. Bardsley, J., Solonen, A., Haario, H. and Laine, M. 2014. Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems. SIAM J. Sci. Comput. 36, A1895A1910. doi:10.1137/140964023
  3. Bengtsson, T., Bickel, P. and Li, B. 2008. Curse of dimensionality revisited: the collapse of importance sampling in very large scale systems. IMS Collect. 2, 316334.
  4. Bickel, P., Bengtsson, T. and Anderson, J. 2008. Sharp failure rates for the bootstrap particle filter in high dimensions. IMS Collect. 3, 318329.
  5. Bocquet, M. 2011. Ensemble Kalman filtering without the intrinsic need for inflation. Nonlin. Process. Geophys. 18, 735750. doi:10.5194/npg-18-735-2011
  6. Bocquet, M. 2016. Localization and the iterative ensemble Kalman smoother. Q. J. Roy. Meteorol. Soc. 142, 10751089. doi:10.1002/qj.2711
  7. Bocquet, M. and Sakov, P. 2013. Joint state and parameter estimation with an iterative ensemble kalman smoother. Nonlin. Process. Geophys. 20, 803818. doi:10.5194/npg-20-803-2013
  8. Bocquet, M. and Sakov, P. 2014. An iterative ensemble Kalman smoother. Q. J. Roy. Meteorol. Soc. 140, 15211535. doi:10.1002/qj.2236
  9. Bonavita, M., Isaksen, L. and Hólm, E. 2012. On the use of EDA background-error variances in the ECMWF 4D-Var. Q. J. Roy. Meteorol. Soc. 138, 15401559. doi:10.1002/qj.1899
  10. Buehner, M. 2005. Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Q. J. Roy. Meteorol. Soc. 131, 10131043. doi:10.1256/qj.04.15
  11. Doucet, A., de Freitas, N. and Gordon, N., eds. 2001. Sequential Monte Carlo Methods in Practice. Springer, New York.
  12. Evensen, G. 2006. Data Assimilation: The Ensemble Kalman Filter. Springer, Berlin Heidelberg.
  13. Evensen, G. 2018. Analysis of iterative ensemble smoothers for solving inverse problems. Comput. Geosci. 22, 885908. doi:10.1007/s10596-018-9731-y
  14. Farchi, A., and Bocquet, M. 2018. Review article: Comparison of local particle filters and new implementations. Nonlinear Process. Geophys. Discuss. 163. doi:10.5194/npg-2018-15
  15. Hamill, T. M., Whitaker, J. and Snyder, C. 2001. Distance-dependent filtering of background covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 27762790. doi:10.1175/1520-0493(2001)129<;2776:DDFOBE>2.0.CO;2
  16. Hodyss, D. and Campbell, W. 2013. Square root and perturbed observation ensemble generation techniques in kalman and quadratic ensemble filtering algorithms. Mon. Wea. Rev. 141, 25612573. doi:10.1175/MWR-D-12-00117.1
  17. Hodyss, D. and Nathan, T. 2007. The role of forcing in the local stability of stationary long waves. Part 1. Linear dynamics. J. Fluid Mech. 576, 349376. doi:10.1017/S0022112006004307
  18. Hodyss, D. and Nathan, T. R. 2006. Instability of variable media to long waves with odd dispersion relations. Commun. Math. Sci. 4, 669676. doi:10.4310/CMS.2006.v4.n3.a10
  19. Hodyss, D., Campbell, W. F. and Whitaker, J. S. 2016. Observation-dependent posterior inflation for the ensemble kalman filter. Mon. Wea. Rev. 144, 26672684. doi:10.1175/MWR-D-15-0329.1
  20. Houtekamer, P. and Mitchell, H. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123136. doi:10.1175/1520-0493(2001)129<;0123:ASEKFF>2.0.CO;2
  21. Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform kalman filter. Phys. D 230, 112126. doi:10.1016/j.physd.2006.11.008
  22. Kuhl, D., Rosmond, T., Bishop, C., McLay, J. and Baker, N. 2013. Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework. Mon. Wea. Rev. 141, 27402758. doi:10.1175/MWR-D-12-00182.1
  23. Lawson, W. and Hansen, J. 2004. Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Wea. Rev. 136, 19661981.
  24. Lee, Y. and Majda, A. 2016. State estimation and prediction using clustered particle filters. Proc. Natl. Acad. Sci. USA. 113, 1460914614. doi:10.1073/pnas.1617398113
  25. Lei, J. and Bickel, P. 2011. A moment matching ensemble filter for nonlinear non-Gaussian data assimilation. Mon. Wea. Rev. 139, 39643973. doi:10.1175/2011MWR3553.1
  26. Liu, C., Xiao, Q. and Wang, B. 2008. An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev. 136, 33633373. doi:10.1175/2008MWR2312.1
  27. Lorenc, A., Bowler, N., Clayton, A., Pring, S. and Fairbairn, D. 2015. Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon. Wea. Rev. 143, 212229. doi:10.1175/MWR-D-14-00195.1
  28. Lorenz, E. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130141. doi:10.1175/1520-0469(1963)020<;0130:DNF>2.0.CO;2
  29. Lorenz, E. N. 1996. Predictability: A problem partly solved, Vol. 1. In: Proceedings of the ECMWF Seminar on predictability, Reading, United Kingdom, 118.
  30. Mandel, J., Cobb, L. and Beezley, J. 2011. On the convergence of the ensemble Kalman filter. Appl. Math. (Prague) 56, 533541. doi:10.1007/s10492-011-0031-2
  31. Metref, S., Cosme, E., Snyder, C. and Brasseur, P. 2014. A non-gaussian analysis scheme using rank histograms for ensemble data assimilation. Nonlin. Process. Geophys. 21, 869885. doi:10.5194/npg-21-869-2014
  32. Morzfeld, M., Hodyss, D. and Poterjoy, J. 2018. Variational particle smoothers and their localization. Q. J. Roy. Meteor. Soc. 144, 806825. doi:10.1002/qj.3256
  33. Morzfeld, M., Hodyss, D. and Snyder, C. 2017. What the collapse of the ensemble Kalman filter tells us about particle filters. Tellus A 69, 1283809. doi:10.1080/16000870.2017.1283809
  34. Penny, S. and Miyoshi, T. 2015. A local particle filter for high dimensional geophysical systems. Nonlinear Process. Geophys. 2, 16311658. doi:10.5194/npgd-2-1631-2015
  35. Posselt, D., Hodyss, D. and Bishop, C. 2014. Errors in ensemble Kalman smoother estimates of cloud microphysical parameters. Mon. Wea. Rev. 142, 16311654. doi:10.1175/MWR-D-13-00290.1
  36. Poterjoy, J. 2016. A localized particle filter for high-dimensional nonlinear systems. Mon. Wea. Rev. 144, 5976. doi:10.1175/MWR-D-15-0163.1
  37. Poterjoy, J. and Anderson, J. 2016. Efficient assimilation of simulated observations in a high-dimensional geophysical system using a localized particle filter. Mon. Wea. Rev. 144, 20072020. doi:10.1175/MWR-D-15-0322.1
  38. Poterjoy, J. and Zhang, F. 2015. Systematic comparison of four-dimensional data assimilation methods with and without the tangent linear model using hybrid background error covariance: E4DVar versus 4DEnVar. Mon. Wea. Rev. 143, 16011621. doi:10.1175/MWR-D-14-00224.1
  39. Poterjoy, J., Sobash, R. and Anderson, J. 2017. Convective-scale data assimilation for the weather research and forecasting model using the local particle filter. Mon. Wea. Rev. 145, 18971918. doi:10.1175/MWR-D-16-0298.1
  40. Poterjoy, J., Wicker, L. and Buehner, M. 2018. Progress toward the application of a localized particle filter for numerical weather prediction. Mon. Wea. Rev.
  41. Potthast, R., Walter, A. and Rhodin, A. 2018. A localised adaptive particle filter within an operational NWP framework. Mon. Wea. Rev.
  42. Reich, S. 2013. A nonparametric ensemble transform method for Bayesian inference. Mon. Wea. Rev. 35, 13371367.
  43. Robert, S. and Künsch, H. R. 2017. Localizing the ensemble Kalman particle filter. Tellus A 69, 1282016. doi:10.1080/16000870.2017.1282016
  44. Sakov, P., Oliver, D. S. and Bertino, L. 2012. An iterative EnKF for strongly nonlinear systems. Mon. Wea. Rev. 140, 19882004. doi:10.1175/MWR-D-11-00176.1
  45. Snyder, C. 2011. Particle filters, the “optimal” proposal and high-dimensional systems. Proceedings of the ECMWF Seminar on Data Assimilation for Atmosphere and Ocean, Reading, UK, 1–10.
  46. Snyder, C., Bengtsson, T., Bickel, P. and Anderson, J. 2008. Obstacles to high-dimensional particle filtering. Mon. Wea. Rev. 136, 46294640. doi:10.1175/2008MWR2529.1
  47. Snyder, C., Bengtsson, T. and Morzfeld, M. 2015. Performance bounds for particle filters using the optimal proposal. Mon. Wea. Rev. 143, 47504761. doi:10.1175/MWR-D-15-0144.1
  48. Talagrand, O. and Courtier, P. 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Q. J. Roy. Meteor. Soc. 113, 13111328. doi:10.1002/qj.49711347812
  49. Tippet, M., Anderson, J., Bishop, C., Hamill, T. and Whitaker, J. 2003. Ensemble square root filters. Mon. Wea. Rev. 131, 14851490. doi:10.1175/1520-0493(2003)131<;1485:ESRF>2.0.CO;2
  50. Tödter, J. and Ahrens, B. 2015. A second-order exact ensemble square root filter for nonlinear data assimilation. Mon. Wea. Rev. 143, 13371367.
  51. Weir, B., Miller, R. N. and Spitz, Y. H. 2013. A potential implicit particle method for high-dimensional systems. Nonlin. Process. Geophys. 20, 10471060. doi:10.5194/npg-20-1047-2013
Language: English
Page range: 1600344 - 1600344
Published on: Jan 1, 2019
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2019 Matthias Morzfeld, Daniel Hodyss, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.