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  2. 地质与地球物理研究所
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  4. 中国科学院地球与行星物理重点实验室
Evaluation on the Quasi-Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

文献类型：期刊论文

作者He, Jianhui1,2,3,4; Yue, Xinan1,2,3,4; Le, Huijun1,2,3,4; Ren, Zhipeng1,2,3,4; Wan, Weixing1,2,3,4
刊名SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
出版日期2020-03-01
卷号18期号:3页码:14
DOI10.1029/2019SW002410
英文摘要In this work, we evaluated the quasi-realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. The slant total electron contents (TECs) from global International Global Navigation Satellite Systems Service ground-based receivers and from the Constellation Observing System for Meteorology, Ionosphere and Climate are assimilated into the system, and the ionosphere is then predicted in advance during the quiet interval of 23 to 27 March 2010. The predicted ionosphere vertical TEC (VTEC) and the critical frequency foF(2) are validated by the Massachusetts Institute of Technology VTEC and global ionosondes network, respectively. We found that the ionosphere forecast quality could be enhanced by optimizing the thermospheric neutral components via the EnKF method. The ionosphere electron density forecast accuracy can be improved by at least 10% for 24 hr. Furthermore, the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) [O/N-2] observations are used to validate the predicted thermosphere [O/N-2]. The validation shows that the [O/N-2] optimized by EnKF has better agreement with the TIMED/GUVI observation. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in addition to our previous observing system simulation experiments by He et al. (2019, ). Plain Language Summary The significance of the coupled thermosphere and ionosphere data assimilation for ionosphere forecasting has been well proven recently. The neutral state variables can be optimized by assimilating ionosphere observations via their correlation represented by the ensemble based error covariance. In this study, the slant total electron contents from global ground-based International Global Navigation Satellite Systems Service receivers and space-based Constellation Observing System for Meteorology, Ionosphere and Climate are ingested into the data assimilation system to evaluate the quasi-realistic ionosphere forecasting capability during the geomagnetic quiet conditions. The Massachusetts Institute of Technology vertical total electron content and global ionosonde network foF(2) observations are chosen to make independent validation. The results show that the ionosphere forecasting capability is enhanced via optimizing the background thermosphere and its effect could last more than 24 hr. In addition, the well-optimized neutral background [O/N-2] by ensemble Kalman filter (EnKF) can be confirmed through the comparison with Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager observations. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in additional to our previous observing system simulation experiments.
WOS关键词SYSTEM SIMULATION EXPERIMENT ; GENERAL-CIRCULATION MODEL ; GLOBAL ASSIMILATION ; SPECIFICATION
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA17010206] ; Open Research Project of Large Research Infrastructures ; Key Research Program of the IGGCAS[IGGCAS-201904] ; National Natural Science Foundation of China[41427901] ; Thousand Young Talents Program of China
WOS研究方向Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000529140700010
出版者AMER GEOPHYSICAL UNION
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Open Research Project of Large Research Infrastructures ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; Key Research Program of the IGGCAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China ; Thousand Young Talents Program of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/96241]  
专题地质与地球物理研究所_中国科学院地球与行星物理重点实验室
通讯作者Yue, Xinan
作者单位1.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Beijing Natl Observ Space Environm, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 He, Jianhui,Yue, Xinan,Le, Huijun,et al. Evaluation on the Quasi-Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm[J]. SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,2020,18(3):14.
APA He, Jianhui,Yue, Xinan,Le, Huijun,Ren, Zhipeng,&Wan, Weixing.(2020).Evaluation on the Quasi-Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm.SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,18(3),14.
MLA He, Jianhui,et al."Evaluation on the Quasi-Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm".SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 18.3(2020):14.

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来源：地质与地球物理研究所

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