| 基于热层电离层耦合数据同化的热层参量估计 |
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| 引用本文： | 何建辉,乐新安.基于热层电离层耦合数据同化的热层参量估计[J].地球物理学报,2020,63(7):2497-2505. |
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| 作者姓名： | 何建辉 乐新安 |
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| 作者单位： | 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 |
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| 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. |
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| 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24h.
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| 关 键 词： | 电离层 集合卡尔曼滤波 数据同化 预报 参量优化 |
| 收稿时间： | 2019-06-21 |
The estimation of thermosphere state variables based on coupled thermosphere and ionosphere data assimilation |
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| HE JianHui,YUE XinAn.The estimation of thermosphere state variables based on coupled thermosphere and ionosphere data assimilation[J].Chinese Journal of Geophysics,2020,63(7):2497-2505. |
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| Authors: | HE JianHui YUE XinAn |
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| Institution: | 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 |
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| 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 (ΣO/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. |
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| Keywords: | Ionosphere Ensemble Kalman filter Data assimilation Prediction Thermosphere parameters optimization |
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