Sequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data
Sequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data
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序列数据同化：数值模拟与大规模观测数据的信息融合
DOI：
10.3217/jucs-012-06-0608
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
N. Hirose
中科院分区：
文献类型：
--
作者：
Kazuyuki Nakamura;T. Higuchi;N. Hirose
数据同化是将一个不完善的模拟模式和大量不完整的观测数据相结合的一种方法。序列数据同化是一种在观测的每一时刻都对模拟变量进行改正的数据同化。集合卡尔曼滤波是为序贯数据同化而发展起来的，在地球物理中经常使用。另一方面，统计中发展和使用的粒子滤波在基于集成的方法方面是相似的，但它具有不同的性质。本文对这两种基于集成的滤波器进行了比较，并通过矩阵表示对其进行了刻画。文中还给出了序贯资料同化在海啸模拟模式中的应用和数值试验。粒子滤波被应用于这一应用。数值试验中对一个错误的海底地形进行了修正，结果表明粒子滤波是一种有用的序列资料同化方法。
Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observa- tion. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter developed and used in statistics is similar in view of ensemble-based method, but it has different properties. In this paper, these two ensemble based filters are compared and characterized through matrix representation. An application of sequential data assimilation to tsunami simu- lation model with a numerical experiment is also shown. The particle filter is employed for this application. An erroneous bottom topography is corrected in the numerical experiment, which demonstrates that the particle filter is useful tool as the sequential data assimilation method.