A data assimilation method for log‐normally distributed observational errors
A data assimilation method for log‐normally distributed observational errors
复制标题
对数正态分布观测误差的数据同化方法
DOI：
10.1256/qj.05.222
复制
发表时间：
2006
影响因子：
8.9
通讯作者：
M. Zupanski
中科院分区：
文献类型：
--
作者：
S. Fletcher;M. Zupanski
在本文中，我们改变了变分数据同化贝叶斯框架中的标准假设，以允许对数正态分布的观测误差。我们解决的问题，统计最好的描述分布的单变量和多变量的情况下，证明我们选择的模式。从这个选择中，我们得到了相关的成本函数，雅可比矩阵和海森矩阵与正常的背景。我们还发现雅可比矩阵的解在模型空间和观测空间都等于零。鉴于我们推导出的Hessian，我们定义了一个预条件子来帮助最小化成本函数。在给定某种类型的成本函数的情况下，我们对此进行扩展以定义预处理器的一般形式。版权所有© 2006年皇家气象学会
In this paper we change the standard assumption made in the Bayesian framework of variational data assimilation to allow for observational errors that are log‐normally distributed. We address the question of which statistic best describes the distribution for the univariate and multivariate cases to justify our choice of the mode. From this choice we derive the associated cost function, Jacobian and Hessian with a normal background. We also find the solution to the Jacobian equal to zero in both model and observational space. Given the Hessian that we derive, we define a preconditioner to aid in the minimization of the cost function. We extend this to define a general form for the preconditioner, given a certain type of cost function. Copyright © 2006 Royal Meteorological Society