Particle Filtering in Data Assimilation and Its Application to Estimation of Boundary Condition of Tsunami Simulation Model

Abstract

We discuss the merit of application of the particle filter compared with the ensemble Kalman filter in data assimilation, as well as its application to tsunami simulation model. The particle filter is one of the ensemble-based methods and is similar to the ensemble Kalman filter that is widely used in sequential data assimilation. We discuss the pros and cons through numerical experiments when the particle filter is used in data assimilation. In next, we review the framework of bottom topography correction based on the tide gauge data. In this procedure, the particle filter was employed to assimilate the tide gauge data, and special localization was used for parameterization. We previously showed the validity of the methods in terms of both attenuation of degeneracy problem and the effectiveness of estimation. We also showed the analysis result of the depth of Yamato Rises in that work. However, the analysis result itself was not sufficiently validated. To validate the analyzed result, we show the result of twin experiment based on artificial bottom topography in this paper. The result fortifies effectiveness of the introduced method for correcting the depth of rise. It also supplements the result of the previous analysis in the Japan Sea.