Embedding reality in a numerical simulation with data assimilation
Embedding reality in a numerical simulation with data assimilation
复制标题
DOI：
--
复制
发表时间：
2011-07
期刊：
影响因子：
--
通讯作者：
T. Higuchi
中科院分区：
文献类型：
--
作者：
T. Higuchi
数据同化（Data assimilation， DA）是一种基于贝叶斯滤波方法，将观测/实验数据嵌入到数值模拟中的综合技术。它具有使仿真真实的适应性，可以自动得到较好的初始条件和边界条件。在统计方法中，数据分析可以用状态空间模型来表述，该模型在时间序列分析、信号处理和控制理论等各个领域引起了研究人员的极大兴趣。就方法论而言，数据分析有两种类型；顺序DA和变分（非顺序）DA。基于集成的顺序数据分析（EnSDA）具有较少人力资源的优势，这是通过插入现有的“前向”仿真代码来实现的。本文简要介绍了EnSDA的最新进展，并对非线性非高斯滤波器之间的关系作了简单的描述。
Data assimilation (DA) is a synthesis technique based on the Bayesian filtering method by embedding observation/experiment data in a numerical simulation. It yields an accommodation ability to make a simulation real, and the better initial and boundary conditions can be automatically obtained. In statistical methodology, DA can be formulated in the state space model that draws much interest of the researchers in various domains such as the time series analysis, signal processing, and control theory. There are two types of DA in terms of a methodology; sequential DA and variational (non-sequential) DA. An ensemble-based sequential DA (EnSDA) has an advantage in terms of less human resources which is achieved by plugging into the existing ”forward” simulation codes. We briefly explain a recent advancement in EnSDA, and give a simple description on the relationship among the nonlinear non-Gaussian filters.