| 顺序数据同化的Bayes滤波框架 |
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| 引用本文： | 李新,摆玉龙.顺序数据同化的Bayes滤波框架[J].地球科学进展,2010,25(5):515-522. |
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| 作者姓名： | 李新 摆玉龙 |
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| 作者单位： | 1. 中国科学院寒区旱区环境与工程研究所,甘肃,兰州,730000
2. 中国科学院寒区旱区环境与工程研究所,甘肃,兰州,730000;西北师范大学物理与电子工程学院,甘肃,兰州,730070 |
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| 基金项目： | 国家自然科学基金，国家杰出青年科学基金，公益性行业(气象)科研专项经费 |
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| 摘 要： | 数据同化是在动力学模型的运行过程中不断融合新的观测信息的方法论,Bayes理论是数据同化的基石.从原理、方法和符号系统为Bayes滤波在数据同化中的应用勾勒一个统一的框架.首先对连续数据同化和顺序数据同化的各种方法做了分类,然后给出了非线性系统顺序数据同化的Bayes递推滤波形式,并在此基础上介绍了典型的顺序数据同化方法--粒子滤波和集合Kalman滤波.粒子滤波实质上是一种基于递推Bayes估计和Monte Carlo模拟的滤波方法,而集合Kalman滤波相当于一种权值相等的粒子滤波.Bayes滤波理论为顺序数据同化提供了更广义的理论框架,从基础的数学理论上揭示了数据同化的基本原理.
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| 关 键 词： | 数据同化 Bayes滤波 集合Kalman滤波 粒子滤波 |
A Bayesian Filter Framework for Sequential Data Assimilation |
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| LI Xin,BAI Yulong.A Bayesian Filter Framework for Sequential Data Assimilation[J].Advance in Earth Sciences,2010,25(5):515-522. |
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| Authors: | LI Xin BAI Yulong |
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| Institution: | 1.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,Lanzhou 730000, China;
2. College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China |
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| Abstract: | Data assimilation is a method in which the observations can be merged with model states by taking advantage of consistent constraints from model physics. The Bayes theory can be considered as the very foundation for data assimilation. The purpose of this paper is to provide a unified theory and notation for the application of Bayesian filter in data assimilation. First, various methods of continuous and sequential data assimilation are classified. Secondly, the sequential data assimilation for nonlinear systems is generalized as a recursive Bayesian filter. Then, two typical sequential data assimilation methods, i.e., the particle filter and the ensemble Kalman filter are represented in the framework of Bayesian filter. The particle filters, in essence, is a Monte Carlo realization of recursive Bayesian filter, and the ensemble Kalman filter is equivalent to the particle filter with equal weights. The theory of Bayesian filter provides a generalized basis for the sequential data assimilation from a more fundamental mathematical viewpoint. |
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| Keywords: | Data assimilation Bayesian filter Ensemble Kalman filter Particle filter |
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