何建辉^1,2,3,4,,
乐新安^1,2,3,4,,
1. 地球与行星物理重点实验室, 中国科学院地质与地球物理研究所, 北京 100029
2. 中国科学院地球科学研究院, 北京 100029
3. 北京空间环境国家野外观测研究站, 中国科学院地质与地球物理研究所, 北京 100029
4. 中国科学院大学地球与行星科学学院, 北京 100049

基金项目: 中国科学院重大科技基础设施开放研究项目"基于子午工程的中低纬大气层-电离层的相互作用研究"和中国科学院地质与地球物理研究所重点部署项目（IGGCAS-201904）资助


详细信息

作者简介: 何建辉, 1992年生, 中国科学院地质与地球物理研究所博士研究生, 目前从事电离层研究.E-mail:jianhuihe@mail.iggcas.ac.cn

通讯作者: 乐新安, 男, 研究员, 博士生导师, 现主要从事电离层相关研究.E-mail:yuexinan@mail.iggcas.ac.cn

中图分类号: P352

收稿日期:2019-06-21
修回日期:2020-05-04
上线日期:2020-07-05

The estimation of thermosphere state variables based on coupled thermosphere and ionosphere data assimilation

HE JianHui^1,2,3,4,,
YUE XinAn^1,2,3,4,,
1. Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2. Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3. Beijing National Observatory of Space Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
4. College of Earth and Planetary Sciences, University of the Chinese Academy of Sciences, Beijing 100049, China


More Information

Corresponding author: YUE XinAn,E-mail:yuexinan@mail.iggcas.ac.cn

MSC: P352

--> Received Date: 21 June 2019
Revised Date: 04 May 2020
Available Online: 05 July 2020


摘要
摘要:本文采用高效集合卡尔曼滤波（EnKF）算法和背景场热层电离层理论模式NCAR-TIEGCM，开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验，对热层参量进行估计.实验结果表明，（1）通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.（2）中性质量密度优化效果在整个同化阶段均有提升，提升百分比能达到40%.（3）积分氧氮比在同化阶段也能得到较好的优化，但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估，结果表明，由于中性成分优化，在地磁平静条件下其预报时间尺度可长达24 h.
关键词: 电离层/
集合卡尔曼滤波/
数据同化/
预报/
参量优化

Abstract:In this paper, an efficient ensemble Kalman filter (EnKF) algorithm and the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model (NCAR-TIEGCM) are used to develop the ensemble Kalman filter data assimilation system. Based on the realistic observational configurations of space-based and ground-based global navigation satellite system (GNSS) ionospheric slant total electron content (TEC) observations and Challenging Minisatellite Payload (CHAMP) and Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) thermosphere measurements, we designed a series of observing system simulation experiments (OSSEs) to evaluate the performance of the system. We found that: (1) The parameters of the thermosphere can be optimized by assimilating ionospheric slant TEC via EnKF algorithm. (2) The performance of neutral mass density optimization is substantial in the whole assimilation stage, and the percentage of improvement can be up to 40%. (3) The integrated O/N₂ ratio (Σ[O/N₂]) can be also optimized well during the assimilation period, but the effect becomes worse in the region where the horizontal gradient of electron density changes dramatically. Finally, the prediction of neutral mass density is evaluated. The results show that the prediction time scale can be up to 24 hours under the condition of geomagnetic quiet due to the optimization of neutral compositions.
Key words:Ionosphere/
Ensemble Kalman filter/
Data assimilation/
Prediction/
Thermosphere parameters optimization



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