Modeling and analysis of ionosphere TEC over China and adjacent areas based on EOF method. Issue 2 (15th July 2019)

• Record Type: Journal Article
• Authors: Li, Shuhui; Zhou, Houxiang; Xu, Jiajia; Wang, Ziqin; Li, Lihua; Zheng, Yanli
• Is Part Of: Advances in space research. Volume 64:Issue 2(2019)
• Page Start: 400
• Page End: 414
• Publication Date: 2019-07-15
• Subjects: Ionosphere -- Total electron content (TEC) -- Empirical orthogonal function (EOF) -- Spatiotemporal characteristics -- IRI-2016; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2019.04.018
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: On the basis of the empirical orthogonal function (EOF) decomposition technique, the spatiotemporal characteristics of the ionospheric total electron content (TEC) from 2007 to 2016 over China and adjacent areas (15°–55°N, 70°–145°E) were studied. The spatial patterns and temporal variations of the TEC were separated by EOF decomposition and can be represented well by the base functions and associated coefficients, respectively. The time variation features of different scales can also be obtained by continually decomposing the coefficients that reflect the temporal variations. Results show that this method is extremely beneficial to the comprehensive analysis of the overall spatiotemporal variations in the ionospheric TEC. The base functions of EOF reflect that the TEC in China and adjacent areas experiences annual and semiannual changes with the latitudinal and longitudinal changes, as well as equatorial anomaly phenomenon. The coefficients of these spatial patterns have evident regularity with the changes in solar activity, season, and local time (LT). After the spatial patterns and the diurnal variations with LT were extracted through a two-layer EOF decomposition, the remaining time variation features were modeled using seasonal variations, solar activity, and geomagnetic activity. Finally, an empirical TEC model was established by incorporating the modeled coefficients and the original EOF base functions. The EOF-based TEC model can demonstrate the temporal and spatial variation characteristics of TEC, and the EOF-based model can obtain significantly smaller modeling errors and achieve better performance in terms of TEC modeling than the International Reference Ionosphere model IRI-2016.