研究论文

数字电离层建设的必要性

  • 乐新安 ,
  • 万卫星
展开
  • 1. 中国科学院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, 北京 100029;
    2. 中国科学院大学地球科学学院, 北京 100049
乐新安,研究员,研究方向为空间物理,电子信箱:yuexinan@mail.iggcas.ac.cn

收稿日期: 2016-12-21

  修回日期: 2017-08-24

  网络出版日期: 2017-10-18

基金资助

青年海外高层次人才引进计划;中国科学院国防科技创新重点部署项目(KGFZD-135-16-01)

Necessity of numerical ionospheric construction

  • YUE Xin'an ,
  • WAN Weixing
Expand
  • 1. Key Laboratory of Earth and Planetary Physics;Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;
    2. College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2016-12-21

  Revised date: 2017-08-24

  Online published: 2017-10-18

摘要

实际应用中,针对不同的电离层电波传播效应,对不同的电离层参量特性,需采用不同手段的观测或不同参量模式的预测预报,解决不同的应用需求。这种"各自为阵"的方式不仅浪费资源,应用效果也较差。为此,本文提出了数字电离层概念,即综合利用中国已有及已经立项的空地多源立体探测体系,在电离层模式化研究的基础上,通过数据融合和数据同化,用数字化方式描述电离层状态参量过去、现在和未来的状况,高效统筹解决各类工程应用中对不同电离层参量的需求。介绍了数字电离层建设的必要性和国内外现状,重点说明了其主要内容和面临的技术瓶颈。

本文引用格式

乐新安 , 万卫星 . 数字电离层建设的必要性[J]. 科技导报, 2017 , 35(19) : 62 -66 . DOI: 10.3981/j.issn.1000-7857.2017.19.008

Abstract

In real applications regarding ionospheric radio wave propagation, the community usually needs appropriate ionospheric parameters on a case by case basis, due to different methods, models, and requirements in the forecast. The kind of ‘lack of coordination’ way not only wastes resources but also degrades the application performance. In this paper we propose a new concept ‘numerical ionosphere’to describe the past, present, and future of ionospheric parameters through assimilating multiple observations into the model to solve the demand of different ionospheric parameters in various engineering applications efficiently and comprehensively.Then, we address the necessity and status of numerical ionosphere development. Finally, we focus on the main content and technical bottleneck of numerical ionosphere.

参考文献

[1] Yue X, Schreiner W S, Kuo Y, et al. Global 3D ionospheric electron density reanalysis based on multisource data assimilation[J]. Journal of Geophysical Research Space Physics, 2012, 117(A9):667-672.
[2] Hu L, Yue X, Ning B. Development of the Beidou ionospheric observa-tion network in China for space weather monitoring[J]. Space Weather, 2017, 15, doi:10.1002/2017SW001636.
[3] Wang C. New chains of space weather monitoring stations in China[J]. Space Weatherthe International Journal of Research & Applications, 2016, 8(8):1-5.
[4] Yue X, Schreiner W S, Pedatella N, et al. Space weather observations by GNSS radio occultation:From FORMOSAT-3/COSMIC to FORMO-SAT-7/COSMIC-2[J]. Space Weather, 2014, 12(11):616-621.
[5] Bilitza D, Mckinnell L A, Reinisch B, et al. The international reference ionosphere today and in the future[J]. Journal of Geodesy, 2011, 85(12):909-920.
[6] Richmond A D, Ridley E C, Roble R G. A thermosphere/ionosphere general circulation model with coupled electrodynamics[J]. Geophysical Research Letters, 2013, 19(6):601-604.
[7] Evensen G. Data Assimilation:The ensemble kalmanfilter[M]. Berlin:Springer, 2009.
[8] 乐新安. 中低纬电离层模拟与数据同化研究[D]. 北京:中国科学院研究生院, 2008. Yue Xinan. Modeling and data assimilation of mid-and low-latitude ionosphere[D]. Beijing:Graduate School, Chinese Academy of Sciences, 2008.
[9] Wang C, Hajj G, Pi X, et al. Development of the global assimilative ion-ospheric model[J]. Radio Science, 2004, 39(1):1-11.
[10] Schunk R W, Scherliess L, Sojka J J, et al. Global assimilation of iono-spheric measurements (GAIM)[J]. Radio Science, 2004, 39(1):429-451.
[11] 万卫星, 宁百齐, 刘立波, 等. 中国电离层TEC现报系统[J]. 地球物理学进展, 2007, 22(4):1040-1045. Wan Weixing, Ning Baiqi, Liu Libo, et al. Nowcasting the ionospheric total electron content over China[J]. Progress in Geophysics, 2007, 22(4):1040-1045.
[12] Aa E, Liu S, Huang W, et al. Regional 3D ionospheric electron densi-ty specification on the basis of data assimilation of ground-based GNSS and radio occultation data[J]. Space Weather-the International Journal of Research & Applications, 2016, 14(6):433-448.
[13] Yu T, Mao T, Wang Y G, et al. Using the GPS observations to recon-struct the ionosphere three-dimensionally with an ionospheric data as-similation and analysis system (IDAAS)[J]. Science China Technologi-cal Science, 2014, 57(11):2210-2219.
[14] 欧明, 甄卫民, 徐继生, 等. 电离层多源数据同化方法研究[J]. 电波科学学报, 2015, 30(1):147-152. Ou Ming, Zhen Weimin, Xu Jisheng, et al. Research on ionospheric multisource data assimilation method[J]. Chinese Journal of Radio Science, 2015, 30(1):147-152.
[15] Jee G, Burns A G, Wang W, et al. Duration of an ionospheric data as-similation initialization of a coupled thermosphere-ionosphere model[J]. Space Weather the International Journal of Research & Applica-tions, 2016, 5(1):1-11.
[16] Hsu C, Matsuo T, Wang W, et al. Effects of inferring unobserved ther-mospheric and ionospheric state variables by using an Ensemble Kal-man Filter on global ionospheric specification and forecasting[J]. Jour-nal of Geophysical Research Space Physics, 2015, 119(11):9256-9267.
[17] Yue X, Wan W, Liu L, et al. Data assimilation of incoherent scatter ra-dar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter[J]. Radio Science, 2016, 42(6):1-20.
[18] Schunk R W, Scherliess L, Eccles V, et al. Space weather forecasting with a multimodel ensemble prediction system (MEPS)[J]. Radio Sci-ence, 2016, 51(7):1157-1165.
[19] Matsuo T, Lee I, Anderson J L. Thermospheric mass density specifica-tion using an ensemble Kalmanfilter[J]. Journal of Geophysical Re-search-Space Physics, 2013, 118(3):1339-1350.
[20] Lei J, Liu L, Wan W, et al. Modeling the behavior of ionosphere above Millstone Hill during the September 21-27, 1998 storm[J]. Jour-nal of Atmospheric and Solar-Terrestrial Physics, 2004, 66(12):1093-1102.
文章导航

/