集合滤波和三维变分混合数据同化方法研究

摘要

发展了一种新的混合数据同化方法——基于集合滤波和三维变分的混合数据同化方法.该方法将集合调整卡尔曼滤波(ensemble adjustment Kalman filter,EAKF)得到的集合样本扰动通过一个转换矩阵的形式直接作用到背景场上,利用顺序滤波的思想得到分析场的一个扰动；然后在三维变分(three dimensional variational analysis,3D-Var)的框架下与观测数据进行拟合,从而给出分析场的最优估计.文中以Lorenz63模型为例,开展了理想数据同化试验,结果表明,相比于集合调整卡尔曼滤波,这种新的混合同化方法可以给出更好的同化结果.%A new hybrid data assimilation scheme based on ensemble adjustment Kalman filter (EAKF) and three-dimensional variational (3D-Var) analysis is developed. In this assimilation scheme, the perturbation of ensemble from EAKF is applied to the background field by using a transformation matrix, thus the perturbation of the analysis field can be obtained by taking advantage of a sequential filter, which will then be optimized by being combined with observations under the framework of 3D-Var. The data assimilation experiment in a perfect case is carried out by using Lorenz-63 model. The results demonstrate that the hybrid data assimilation scheme performs better than EAKF.