Variational quality control of non-Gaussian innovations and its parametric optimizations for the GRAPES m3DVAR system  

作　　者：Jie HE Yang SHI Boyang ZHOU Qiuping WANG Xulin MA 

机构地区：[1]Guangzhou Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510640,China [2]Guangdong Meteorological Observatory,Guangzhou 510640,China [3]Qingdao Air Traffic Management Station of Civil Aviation of China,Qingdao 266108,China [4]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Key Laboratory of Meteorological Disaster,Nanjing University of Information Science and Technology,Nanjing 210044,China

出　　处：《Frontiers of Earth Science》2023年第2期620-631,共12页地球科学前沿（英文版）

基　　金：sponsored by the National Key R&D Program of China(Nos.2018YFC1506702 and 2017YFC1502000).

摘　　要：The magnitude and distribution of observation innovations,which have an important impact on the analyzed accuracy,are critical variables in data assimilation.Variational quality control(VarQC)based on the contaminated Gaussian distribution(CGD)of observation innovations is now widely used in data assimilation,owing to the more reasonable representation of the probability density function of innovations that can sufficiently absorb observations by assigning different weights iteratively.However,the inaccurate parameters prevent VarQC from showing the advantages it should have in the GRAPES(Global/Regional Assimilation and PrEdiction System)m3DVAR system.Consequently,the parameter optimization methods are considerable critical studies to improve VarQC.In this paper,we describe two probable CGDs to include the non-Gaussian distribution of actual observation errors,Gaussian plus flat distribution and Huber norm distribution.The potential optimization methods of the parameters are introduced in detail for different VarQCs.With different parameter configurations,the optimization analysis shows that the Gaussian plus flat distribution and the Huber norm distribution are more consistent with the long-tail distribution of actual innovations compared to the Gaussian distribution.The VarQC’s cost and gradient functions with Huber norm distribution are more reasonable,while the VarQC’s cost function with Gaussian plus flat distribution may converge on different minimums due to its nonconcave properties.The weight functions of two VarQCs gradually decrease with the increase of innovation but show different shapes,and the VarQC with Huber norm distribution shows more elasticity to assimilate the observations with a high contamination rate.Moreover,we reveal a general derivation relationship between the CGDs and VarQCs.A novel schematic interpretation that classifies the assimilated data into three categories in VarQC is presented.They are conducive to the development of a new VarQC method in the future.

关 键 词：data assimilation variational quality control contaminated Gaussian distribution non-Gaussian distribution INNOVATION 

分 类 号：P456.7[天文地球—大气科学及气象学]