1,898
Views
26
CrossRef citations to date
0
Altmetric
Article

Localizing the Ensemble Kalman Particle Filter

&
Article: 1282016 | Received 09 May 2016, Accepted 03 Jan 2017, Published online: 08 Mar 2017

References

  • Ades, M. and van Leeuwen, P. J. 2013. An exploration of the equivalent weights particle filter. Q. J. Roy. Meteorol. Soc. 139(672), 820–840.
  • Alspach, D. L. and Sorenson, H. W. 1972. Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Trans. Autom. Control 17(4), 439–448.
  • Bauer, P., Thorpe, A. and Brunet, G. 2015. The quiet revolution of numerical weather prediction. Nature 525(7567), 47–55.
  • Bengtsson, T., Snyder, C. and Nychka, D. 2003. Toward a nonlinear ensemble filter for high-dimensional systems. J. Geophys. Res. Atmos. 108(D24). Online at: http://onlinelibrary.wiley.com/doi/10.1029/2002JD002900/full
  • Carpenter, J., Clifford, P. and Fearnhead, P. 1999. Improved particle filter for nonlinear problems. IEE Proc. Radar Sonar Navig. 146(1), 2–7.
  • Crisan, D. 2001. Particle filters – A theoretical perspective. In: Sequential Monte Carlo Methods in Practice (eds. A. Doucet, N. D. Freitas and N. Gordon), Statistics for Engineering and Information Science. Springer, New York, pp. 17–41.
  • Doucet, A., De Freitas, N. and Gordon, N. 2001. Sequential Monte Carlo Methods in Practice. Springer, New York.
  • Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans 99(C5), 10143–10162.
  • Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter. Springer, New York.
  • Frei, M. and Künsch, H. R. 2013. Bridging the ensemble Kalman and particle filters. Biometrika 100(4), 781–800.
  • Gaspari, G. and Cohn, S. E. 1999. Construction of correlation functions in two and three dimensions. Q. J. Roy. Meteorol. Soc. 125(554), 723–757.
  • Gneiting, T. and Katzfuss, M. 2014. Probabilistic forecasting. Ann. Rev. Stat. Appl. 1(1), 125–151.
  • Gneiting, T. and Raftery, A. E. 2007. Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102(477), 359–378.
  • Gordon, N., Salmond, D. and Smith, A. 1993. Novel approach to nonlinear non-Gaussian Bayesian state estimation. IEE Proc. F Radar Signal Process. 140(2), 107–113.
  • Hamill, T. M., Whitaker, J. S. and Snyder, C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Weather Rev. 129(11), 2776–2790.
  • Haslehner, M., Janjić, T. and Craig, G. C. 2016. Testing particle filters on simple convective-scale models Part 2: A modified shallow-water model. Q. J. Roy. Meteorol. Soc. 142(697), 1628–1646.
  • Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 126(3), 796–811.
  • Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 129(1), 123–137.
  • Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230(1–2), 112–126.
  • Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45.
  • Kalman, R. E. and Bucy, R. S. 1961. New results in linear filtering and prediction theory. J. Basic Eng. 83(1), 95–108.
  • Künsch, H. R. 2005. Recursive Monte Carlo filters: Algorithms and theoretical analysis. Ann. Stat. 33(5), 1983–2021.
  • van Leeuwen, P. J. 2009. Particle filtering in geophysical systems. Mon. Weather Rev. 137(12), 4089–4114.
  • van Leeuwen, P. J. 2010. Nonlinear data assimilation in geosciences: An extremely efficient particle filter. Q. J. Roy. Meteorol. Soc. 136(653), 1991–1999.
  • Lei, J. and Bickel, P. 2011. A moment matching ensemble filter for nonlinear non-Gaussian data assimilation. Mon. Weather Rev. 139(12), 3964–3973.
  • Liu, J. S. 1996. Metropolized independent sampling with comparisons to rejection sampling and importance sampling. Stat. Comput. 6(2), 113–119.
  • 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.
  • Musso, C., Oudjane, N. and Le Gland, F. 2001. Improving regularised particle filters. In: Sequential Monte Carlo Methods in Practice (eds. A. Doucet, N. De Freitas and N. Gordon). Springer, New York, pp. 247–271.
  • Nakano, S. 2014. Hybrid algorithm of ensemble transform and importance sampling for assimilation of non-Gaussian observations. Tellus A 66, 21429.
  • Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., co-authors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A 56(5), 415–428.
  • Papadakis, N., Memin, E., Cuzol, A. and Gengembre, N. 2010. Data assimilation with the weighted ensemble Kalman filter. Tellus A 62A, 673–697.
  • Pitt, M. K. and Shephard, N. 1999. Filtering via simulation: Auxiliary particle filters. J. Am. Stat. Assoc. 94(446), 590–599.
  • Poterjoy, J. 2016. A localized particle filter for high-dimensional nonlinear systems. Mon. Weather Rev. 144(1), 59–76.
  • Reich, S. 2013. A guided sequential Monte Carlo method for the assimilation of data into stochastic dynamical systems. In: Recent Trends in Dynamical Systems (eds. A. Johann, H.-P. Kruse, F. Rupp and S. Schmitz), Vol. 35, Springer Proceedings in Mathematics & Statistics. Springer, Basel, pp. 205–220.
  • Robert, S. and Künsch, H. R. 2016. Localization in high-dimensional Monte Carlo filtering. ArXiv:1610.03701.
  • Sigrist, F., Künsch, H. R. and Stahel, W. A. 2012. A dynamic nonstationary spatio-temporal model for short term prediction of precipitation. Ann. Appl. Stat. 6(4), 1452–1477.
  • Snyder, C., Bengtsson, T., Bickel, P. and Anderson, J. 2008. Obstacles to high-dimensional particle filtering. Mon. Weather Rev. 136(12), 4629–4640.
  • Snyder, C., Bengtsson, T. and Morzfeld, M. 2015. Performance bounds for particle filters using the optimal proposal. Mon. Weather Rev. 143(11), 4750–4761.
  • Stidd, C. K. 1973. Estimating the precipitation climate. Water Resour. Res. 9(5), 1235–1241.
  • Würsch, M. 2014. Testing data assimilation methods in idealized models of moist atmospheric convection. Ph.D. thesis, Ludwig-Maximilians-Universität München.
  • Würsch, M. and Craig, G. C. 2014. A simple dynamical model of cumulus convection for data assimilation research. Meteorol. Z. 23(5), 483–490.