1,142
Views
7
CrossRef citations to date
0
Altmetric
Research Article

The smoother extension of the nonlinear ensemble transform filter

, , ORCID Icon & ORCID Icon
Article: 1327766 | Received 20 Feb 2017, Accepted 03 May 2017, Published online: 30 May 2017

References

  • Anderson, J. L. 2001. An ensemble adjustment kalman filter for data assimilation. Mon. Wea. Rev. 129, 2884–2903.
  • Bishop, C., Etherton, B. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev. 129(3), 420–436.
  • Carval, T., Keeley, R., Takatsuki, Y., Yoshida, T., Schmid, C. co-authors. 2015. Argo user’s manual V3.2, 124pp. http://doi.org/10.13155/29825
  • Cohn, S., Sivakumaran, N. and Todling, R. 1994. A fixed-lag Kalman Smoother for retrospective data assimilation. Mon. Wea. Rev. 122(12), 2838–2867.
  • Cosme, E., Brankart, J. M., Verron, J., Brasseur, P. and Krysta, M. 2010. Implementation of a reduced rank square-root smoother for high resolution ocean data assimilation. Ocean Model. 33(1–2), 87–100.
  • Cosme, E., Verron, J., Brasseur, P., Blum, J. and Auroux, D. 2012. Smoothing problems in a bayesian framework and their linear Gaussian solutions. Mon. Wea. Rev. 140(2), 683–695.
  • Danilov, S., Kivman, G. and Schröter, J. 2004. A finite-element ocean model: principles and evaluation. Ocean Model. 6(2), 125–150.
  • de Wiljes, J., Acevedo, W. and Reich, S. 2016. Second-order accurate ensemble transform particle filters. arXiv:1608.08179.
  • Durrant, T. H., Greenslade, D. J. M. and Simmonds, I. 2009. Validation of Jason-1 and Envisat remotely sensed wave heights. J. Atmos. Ocean. Technol. 26(1), 123–134.
  • Evensen, G. 2006. Data Assimilation: The Ensemble Kalman Filter Springer, Berlin Heidelberg.
  • Evensen, G. and van Leeuwen, P. J. 2000. An Ensemble Kalman Smoother for Nonlinear Dynamics. Mon. Wea. Rev. 128(6), 1852–1867.
  • Gaspari, G. and Cohn, S. E. 1999. Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc. 125, 723–757.
  • Gneiting, T., Balabdaoui, F. and Raftery, A. E. 2007. Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 69(2), 243–268.
  • Godsill, S., Doucet, A. and West, M. 2004. Monte Carlo smoothing for nonlinear time series. J. Am. Stat. Association 99(465), 156–168.
  • Gordon, N., Salmond, D. J. and Smith, A. F. M. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings F (Radar and Signal Processing) 140, 107–113.
  • Hoteit, I., Pham, D.-T. and Blum, J. 2002. A simplified reduced order Kalman filtering and application to altimetric data assimilation in tropical Pacific. J. Mar. Syst. 36, 101–127.
  • Hunt, B., Kostelich, E. and Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230, 112–126.
  • Janjić, T., Nerger, L., Albertella, A., Schröter, J. and Skachko, S. 2011. On domain localization in ensemble-based Kalman filter algorithms. Mon. Wea. Rev. 139, 2046–2060.
  • Kalnay, E. 2002. Atmospheric Modeling, Data Assimilation and Predictability, 1st ed. Cambridge University Press, Cambridge, 357 pp.
  • Kalnay, E. and Yang, S. C. 2010. Accelerating the spin-up of Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc. 136(651), 1644–1651.
  • Khare, S. P., Anderson, J. L., Hoar, T. J. and Nychka, D. 2008. An investigation into the application of an ensemble Kalman smoother to high-dimensional geophysical systems. Tellus Ser. A: Dyn. Meteo. Oceanography 60A(1), 97–112.
  • Kirchgessner, P., Nerger, L. and Bunse-Gerstner, A. 2014. On the choice of an optimal localization radius in ensemble Kalman filter methods. Mon. Wea. Rev. 142(6), 2165–2175.
  • Kitagawa, G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graphical Stat. 5(1), 1–25.
  • Lei, J. and Bickel, P. 2011. A moment matching ensemble filter for nonlinear non-Gaussian data assimilation. Mon. Wea. Rev. 139, 3964–3973.
  • Lorenz, E. 1996. Predictability: A Problem Partly Solved. Proceedings Seminar on Predictability. ECMWF, Reading, pp. 1–18.
  • Madec, G. and the NEMO Team. 2012. NEMO ocean engine. Note du Pôle de modélisation. Institut Pierre-Simon Laplace (IPSL) Version 3.3. 27, 332.
  • Nerger, L. 2015. On serial observation processing on localized ensemble Kalman filters. Mon. Wea. Rev. 143, 1554–1567.
  • Nerger, L. and Hiller, W. 2013. Software for ensemble-based data assimilation systems-implementation strategies and scalability. Comput. & Geosci. 55, 110–118.
  • Nerger, L., Janjic, T., Schroeter, J. and Hiller, W. 2012. A regulated localization scheme for ensemble-based Kalman filters. Quart. J. Roy. Meteor. Soc. 138, 802–812.
  • Nerger, L., Schulte, S. and Bunse-Gerstner, A. 2014. On the influence of model nonlinearity and localization on ensemble Kalman smoothing. Quart. J. Roy. Meteor. Soc. 140, 2249–2259.
  • Pham, D. T. 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon. Wea. Rev. 129, 1194–1207.
  • Posselt, D. J., Hodyss, D. and Bishop, C. H. 2014. Errors in Ensemble Kalman smoother estimates of cloud microphysical parameters. Mon. Wea. Rev. 142(4), 1631–1654.
  • Poterjoy, J. 2016. A localized particle filter for high-dimensional nonlinear systems. Mon. Wea. Rev. 144, 59–76.
  • Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R. and co-authors. 2012. TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci. 8(4), 633–656.
  • Sakov, P. and Oke, P. R. 2008. Implications of the form of the ensemble transformation in the ensemble square root filters. Mon. Wea. Rev. 136, 1042–1053.
  • Szunyogh, I. 2014. Applicable Atmospheric Dynamics: Techniques for the Exploration of Atmospheric Dynamics. World Scientific, Singapore.
  • Tödter, J. (2015). Derivation and characterization of a new filter for nonlinear high-dimensional data assimilation, Ph.D. thesis, Goethe University, 187 pp. Frankfurt/Main, http://d-nb.info/1074192494/34
  • Tödter, J. and Ahrens, B. 2015. A second-order exact ensemble square root filter for nonlinear data assimilation. Mon. Wea. Rev. 143(4), 1347–1367.
  • Tödter, J., Kirchgessner, P., Nerger, L. and Ahrens, B. 2016. Assessment of a nonlinear ensemble transform filter for high-dimensional data assimilation. Mon. Wea. Rev. 144, 409–427.
  • van Leeuwen, P. J. 2009. Particle filtering in geophysical systems. Mon. Wea. Rev. 137(12), 4089–4114.
  • van Leeuwen, P. J. and Evensen, G. 1996. Data assimilation and inverse methods in terms of a probabilistic formulation. Mon. Wea. Rev. 124, 2898–2913.
  • Whitaker, J. and Hamill, T. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1722.
  • Yan, Y., Barth, A. and Beckers, J. M. 2014. Comparison of different assimilation schemes in a sequential Kalman filter assimilation system. Ocean Model. 73, 123–137.