Correlation between System and Observation Errors in Data Assimilation
Correlation between System and Observation Errors in Data Assimilation
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DOI：
10.1175/mwr-d-17-0331.1
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发表时间：
2018-09-01
影响因子：
3.2
通讯作者：
Sauer, Timothy
中科院分区：
文献类型：
--
作者：
Berry, Tyrus;Sauer, Timothy
准确了解两种类型的噪声（系统噪声和观测噪声）是贝叶斯过滤方法的一个重要方面。传统上，这种知识反映在两个噪声贡献的个体协方差矩阵中，而系统和观测噪声之间的相关性被忽略。我们认为，在实际问题中，系统误差和观测误差不太可能不相关，特别是对于地球物理驱动的例子，其中误差主要由模型和观测截断决定。此外，研究表明，考虑滤波算法中的互相关性，例如在相关系综卡尔曼滤波器中，可以显着提高来自典型动态系统的数据的滤波精度。特别是，我们讨论了两种类型的误差相对于个体协方差最大相关的极端情况。
Accurate knowledge of two types of noise, system and observational, is an important aspect of Bayesian filtering methodology. Traditionally, this knowledge is reflected in individual covariance matrices for the two noise contributions, while correlations between the system and observational noises are ignored. We contend that in practical problems, it is unlikely that system and observational errors are uncorrelated, in particular for geophysically motivated examples where errors are dominated by model and observation truncations. Moreover, it is shown that accounting for the cross correlations in the filtering algorithm, for example in a correlated ensemble Kalman filter, can result in significant improvements in filter accuracy for data from typical dynamical systems. In particular, we discuss the extreme case where the two types of errors are maximally correlated relative to the individual covariances.