Parameter Optimization of Regularization Variational Merging and Its Application in GNSS/MET Water Vapor

• 作者: Ding Xia Liu Huilan Wang Gen Zhou Shuxue
• 来源期刊: 气象与环境研究:英文版
• 学科: 地球科学
• 关键词: VARIATIONAL MERGING L1 NORM PARAMETER optimization Precipitable water vapor REGULARIZATION PARAMETER
• 年，卷（期）: 2019,（2）
• 页码范围: 44-50
• 页数: 7页
• 分类号: P228.4

摘要

The paper discusses the core parameters of the 3 D and 4 D variational merging based on L1 norm regularization,namely optimization characteristic correlation length of background error covariance matrix and regularization parameter. Classical 3 D/4 D variational merging is based on the theory that error follows Gaussian distribution. It involves the solution of the objective functional gradient in minimization iteration,which requires the data to have continuity and differentiability. Classic 3 D/4 D-dimensional variational merging method was extended,and L1 norm was used as the constraint coupling to the classical variational merged model. Experiment was carried out by using linear advection-diffusion equation as four-dimensional prediction model,and parameter optimization of this method is discussed. Considering the strong temporal and spatial variation of water vapor,this method is further applied to the precipitable water vapor( PWV) merging by calculating reanalysis data and GNSS retrieval.Parameters were adjusted gradually to analyze the influence of background field on the merging result,and the experiment results show that the mathematical algorithm adopted in this paper is feasible.