HR: 1340h
AN: SM13A-0331    [Abstracts]
TI: Ensemble-based Filtering and Smoothing Methods for the Sequential Data Assimilation with Highly Nonlinear Observation System
AU: Nakamura, K
EM: nakakazu@ism.ac.jp
AF: Department of Statistical Science, The Graduate University for Advanced Studies, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569 Japan
AU: Nakamura, K
EM: nakakazu@ism.ac.jp
AF: Research Organization of Information and Systems, The Institute of Statistical Mathematics/JST CREST, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569 Japan
AU: * Ueno, G
EM: gen@ism.ac.jp
AF: Research Organization of Information and Systems, The Institute of Statistical Mathematics/JST CREST, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569 Japan
AU: Nakano, S
EM: shiny@ism.ac.jp
AF: Research Organization of Information and Systems, The Institute of Statistical Mathematics/JST CREST, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569 Japan
AU: Higuchi, T
EM: higuchi@ism.ac.jp
AF: Research Organization of Information and Systems, The Institute of Statistical Mathematics/JST CREST, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569 Japan
AB: Sequential data assimilation which is methodology and concept used mainly in the meteorology and oceanography, aims at accommodating physical variables of simulation models to observation data. The Ensemble Kalman Filter (EnKF) was invented and is used in sequential data assimilation. This procedure is based on the second order statistics and it cannot deal with these systems directly if observed data are nonlinear transformation of states. This problem is resolved by extending the state vector, but this cannot reflect ensemble states completely. On the other hand, it is well known that the Particle Filter (PF), which is developed in statistical field, can deal with higher order statistics and nonlinear transformed states without extension. Both of them are ensemble-based filtering methods and can be extended to fixed lag smoother (the EnKS and the Particle Smoother(PS)). This research demonstrates that the PF and the PS are superior to the EnKF and the EnKS in assimilating nonlinear observation by numerical experiments.
UR: http://tswww.ism.ac.jp/higuchi/CREST/index.html
DE: 2722 Forecasting (7924, 7964)
DE: 2753 Numerical modeling
DE: 2794 Instruments and techniques
DE: 4494 Instruments and techniques
DE: 7924 Forecasting (2722)
SC: SPA-Magnetospheric Physics [SM]
MN: Fall Meeting 2005