No. 977

Title:

Ensemble-based Nonlinear Filters for
Sequential Data Assimilation and Their Applications

Author(s):

Nakamura, Kazuyuki (The Graduate University for Advanced Studies);
Higuchi, Tomoyuki (The Institute of Statistical Mathematics);
Hirose, Naoki (Research Institute for Applied Mechanics, Kyushu University);
Ueno, Genta (The Institute of Statistical Mathematics)

Key words:

data assimilation; state estimation; Kalman filters; Monte Carlo method

Abstract:

    Sequential data assimilation which is methodology and concept used in the meteorology and the oceanography, aims at accommodating physical variables of simulation models to observation data. The ensemble Kalman filter is widely used in sequential data assimilation field. This filter is similar to the Particle filter in that both are ensemble-based and sequential method, but the properties of these differ. In this research, these properties are shown through numerical experiments. We also applied these filters to simulation model for tsunami. Correction of bottom topography and tsunami height are conducted through these procedures.