Multiplicative and Additive Incremental Variational Data Assimilation for Mixed Lognormal–Gaussian Errors
Multiplicative and Additive Incremental Variational Data Assimilation for Mixed Lognormal–Gaussian Errors
复制标题
混合对数正态-高斯误差的乘法和加法增量变分数据同化
DOI：
10.1175/mwr-d-13-00136.1
复制
发表时间：
2014
影响因子：
3.2
通讯作者：
Andrew S. Jones
中科院分区：
文献类型：
--
作者：
S. Fletcher;Andrew S. Jones
AbstractAn进步，高斯为基础的三维和四维变分资料同化（3D和4DVAR，分别）业务可行的数值天气预报的增量公式的引入。这减少了计算成本的变分方法通过寻找一个小的增量的背景状态，其演变是近似线性的。本文给出了具有对数正态分布和对数正态-高斯混合分布背景和观测误差的三维和四维DVAR增量公式。由于对数正态分布具有几何性质，证明了切线线性模型（TLM）的几何版本，使成本函数的观测分量相对于几何增量线性化。这与基于混合分布的成本函数的加性TLM相结合。文中给出了在不同观测误差方差下，采用混合增量方案和Lorenz'63模式的结果。
AbstractAn advance that made Gaussian-based three- and four-dimensional variational data assimilation (3D- and 4DVAR, respectively) operationally viable for numerical weather prediction was the introduction of the incremental formulation. This reduces the computational costs of the variational methods by searching for a small increment to a background state whose evolution is approximately linear. In this paper, incremental formulations for 3D- and 4DVAR with lognormal and mixed lognormal–Gaussian-distributed background and observation errors are presented. As the lognormal distribution has geometric properties, a geometric version for the tangent linear model (TLM) is proven that enables the linearization of the observational component of the cost functions with respect to a geometric increment. This is combined with the additive TLM for the mixed distribution–based cost function. Results using the mixed incremental scheme with the Lorenz’63 model are presented for different observational error variances...