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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 4
Nonlin. Processes Geophys., 14, 395–408, 2007
https://doi.org/10.5194/npg-14-395-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Nonlin. Processes Geophys., 14, 395–408, 2007
https://doi.org/10.5194/npg-14-395-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  16 Jul 2007

16 Jul 2007

Merging particle filter for sequential data assimilation

S. Nakano1,2, G. Ueno1,2, and T. Higuchi1,2 S. Nakano et al.
  • 1The Institute of Statistical Mathematics, Research Organization of Information and Systems, Japan
  • 2Japan Science and Technology Agency, Japan

Abstract. A new filtering technique for sequential data assimilation, the merging particle filter (MPF), is proposed. The MPF is devised to avoid the degeneration problem, which is inevitable in the particle filter (PF), without prohibitive computational cost. In addition, it is applicable to cases in which a nonlinear relationship exists between a state and observed data where the application of the ensemble Kalman filter (EnKF) is not effectual. In the MPF, the filtering procedure is performed based on sampling of a forecast ensemble as in the PF. However, unlike the PF, each member of a filtered ensemble is generated by merging multiple samples from the forecast ensemble such that the mean and covariance of the filtered distribution are approximately preserved. This merging of multiple samples allows the degeneration problem to be avoided. In the present study, the newly proposed MPF technique is introduced, and its performance is demonstrated experimentally.

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