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Activity Number:
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512
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #309955 |
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Title:
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Estimation of Sea Depth by Data Assimilation with Tsunami Simulation Model
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Author(s):
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Kazuyuki Nakamura*+ and Tomoyuki Higuchi and Naoki Hirose
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Companies:
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The Graduate University for Advanced Studies and The Institute of Statistical Mathematics and Kyushu University
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Address:
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4-6-7 Minami-Azabu, Tokyo, International, 1068569, Japan
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Keywords:
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data assimilation ; particle filter ; state space model ; tsunami
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Abstract:
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A tsunami simulation model can forecast the arrival time and the height of tsunamis. The sea bottom topography is included in the model as the boundary condition. However, the bottom topography datasets have errors which cause inaccurate forecasts. Therefore, we should modify the bottom topography in the model to obtain more precise results. To correct the topography, we have introduced a data assimilation framework in which a tsunami simulation model and tide gauge records are combined. We will demonstrate the framework in terms of nonlinear state space model and present the result applied to the Okushiri Tsunami, which occurred in the Japan Sea in 1993. The particle filter is used in this estimation. The result indicates that an area in the Japan Sea might be shallower than the existing topography datasets.
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