Articles | Volume 23, issue 6
Research article
04 Nov 2016
Research article |  | 04 Nov 2016

A local particle filter for high-dimensional geophysical systems

Stephen G. Penny and Takemasa Miyoshi

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Cited articles

Abarbanel, H. D. I., Creveling, D. R., Farsian, R., and Kostuk, M.: Dynamical State and Parameter Estimation, SIAM J. Appl. Dyn. Syst., 8, 1341–1381,, 2009.
Ades, M. and van Leeuwen, P. J.: An exploration of the equivalent weights particle filter, Q. J. Roy. Meteorol. Soc., 139, 820–840, 2013.
Anderson, J.: An ensemble adjustment kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001.
Atkins, E., Morzfeld, M., and Chorin, A. J.: Implicit Particle Methods and their Connection with Variational Data Assimilation, Mon. Weather Rev., 141, 1786–1803, 2013.
Bengtsson, T., Snyder, C., and Nychka, D.: Toward a nonlinear ensemble filter for high-dimensional systems, J. Geophys. Res., 108, STS2.1–STS2.10, 2003.
Short summary
Particle filters in their basic form have been shown to be unusable for large geophysical systems because the number of required particles grows exponentially with the size of the system. We have applied the ideas of localized analyses at each model grid point and use ensemble weight smoothing to blend each local analysis with its neighbors. This new local particle filter (LPF) makes large geophysical applications tractable for particle filters and is competitive with a popular EnKF alternative.