An empirical orthogonal function model of total electron content over China

Mao, Tian; Wan, Weixing; Yue, Xinan; Sun, Luming; Guo, Jianpeng

doi:10.1029/2007rs003629

Cited by 72 publications

(51 citation statements)

References 34 publications

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“…To analyze data of GPS-TEC, we used the method proposed by Ping et al [16,17], which was used to derive the ionospheric information from the Japanese GEONET network of about 1400 stations. To obtain the TEC map, we used the improved Kriging method proposed by Mao [18,19]. The temporal resolution of this method is 5 minutes, and the spatial resolution is 0.5°× 0.5° on the latitudinal and longitudinal grids.…”

Section: Data and Resultsmentioning

confidence: 99%

Effects of typhoon Matsa on ionospheric TEC

et al. 2009

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The effects of typhoon Matsa on the ionosphere are studied by using GPS-TEC data observed at about 50 GPS stations. It is shown that the ionosphere has been already influenced and TEC tends to increase before the landing of Matsa, and the difference of TEC from its monthly median over the typhoon area is about 5 TECU. With the landing of Matsa, both the magnitude and the area of increased TEC decrease. One day after the landing of Matsa, TEC reaches its minimum and is lower than the monthly median. In comparison of TEC along the typhoon's path with that along three reference paths far from the typhoon, it is found that typhoon's impact on TEC can be fully distinguished. The evolution of TEC variation has the same tendency as reported typhoon-induced foF2.typhoon Matsa, ionosphere, GPS, coupling, TEC map Citation:

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Section: Data and Resultsmentioning

confidence: 99%

Effects of typhoon Matsa on ionospheric TEC

et al. 2009

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“…Dvinskikh (1988) first introduced EOF analysis into the empirical modeling of the ionospheric parameters. It has been confirmed by many researchers that EOF analysis is a powerful method for ionospheric modeling and data analysis (e.g., Singer and Dvinskikh, 1991;Daniell et al, 1995;Matsuo et al, 2002Matsuo et al, , 2005Marsh et al, 2004;Zhao et al, 2005;Zapfe et al, 2006;Liu et al, 2008;Mao et al, 2008;Zhang et al, 2009;Matsuo and Forbes, 2010, and more). According to EOF theory, the variation of the ionospheric parameters are attributed to different factors which can be extracted and separated in terms of their relative "contribution" with respect to ionospheric parameters.…”

Section: Brief Introduction Of Eof Analysis Methodsmentioning

confidence: 98%

Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis

et al. 2011

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Abstract.A similar-parameters interpolation method and an empirical orthogonal function analysis are used to construct empirical models for the ionospheric foF2 by using the observational data from three ground-based ionosonde stations in Japan which are Wakkanai (Geographic 45.4 • N, 141.7 • E), Kokubunji (Geographic 35.7 • N, 140.1 • E) and Yamagawa (Geographic 31.2 • N, 130.6 • E) during the years of [1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986][1987]. The impact of different drivers towards ionospheric foF2 can be well indicated by choosing appropriate proxies. It is shown that the missing data of original foF2 can be optimal refilled using similar-parameters method. The characteristics of base functions and associated coefficients of EOF model are analyzed. The diurnal variation of base functions can reflect the essential nature of ionospheric foF2 while the coefficients represent the long-term alteration tendency. The 1st order EOF coefficient A 1 can reflect the feature of the components with solar cycle variation. A 1 also contains an evident semi-annual variation component as well as a relatively weak annual fluctuation component. Both of which are not so obvious as the solar cycle variation. The 2nd order coefficient A 2 contains mainly annual variation components. The 3rd order coefficient A 3 and 4th order coefficient A 4 contain both annual and semi-annual variation components. The seasonal variation, solar rotation oscillation and the small-scale irregularities are also included in the 4th order coefficient A 4 . The amplitude range and developing tendency of all these coefficients depend on the level of solar activity and geomagnetic activity. The reliability and validity of EOF model are verified by comparison with observational data and with International Reference Ionosphere (IRI). The agreement between observations and EOF model is quite well, indicating that the EOF model can reflect the major changes and the temporal distribution characteristicsCorrespondence to: D.-H. Zhang (zhangdh@pku.edu.cn) of the mid-latitude ionosphere of the Sea of Japan region. The error analysis processes imply that there are seasonal anomaly and semi-annual asymmetry phenomena which are consistent with pre-existing ionosphere theory.

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“…His pioneer work was followed by other works (e.g., Singer and Taubenheim, 1990;Bossy and Rawer, 1990;Singer and Dvinskikh, 1991;Dvinskikh and Naidenova, 1991). It has been shown by many researchers that the EOF analysis is a powerful method in the ionospheric data analysis and empirical modelling (e.g., Dvinskikh, 1988;Singer and Dvinskikh, 1991;Dvinskikh and Naidenova, 1991;Daniell et al, 1995;Marsh et al, 2004;Matsuo et al, 2002Matsuo et al, , 2005Mao et al, 2005Mao et al, , 2008Materassia and Mitchell, 2005;Zhao et al, 2005;Zapfe et al, 2006, Liu et al, 2008Wan et al, 2009, and many more).…”

Section: Fundamental Of Eof Analysis Methodsmentioning

confidence: 99%

A global model of the ionospheric F2 peak height based on EOF analysis

et al. 2009

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Abstract. The ionospheric F2 peak height hmF2 is an important parameter that is much needed in ionospheric research and practical applications. In this paper, an attempt is made to develop a global model of hmF2. The hmF2 data, used to construct the global model, are converted from the monthly median hourly values of the ionospheric propagation factor M(3000)F2 observed by ionosondes/digisondes distributed globally, based on the strong anti-correlation existed between hmF2 and M(3000)F2. The empirical orthogonal function (EOF) analysis method, combined with harmonic function and regression analysis, is used to construct the model. The technique used in the global modelling involves two layers of EOF analysis of the dataset. The first layer EOF analysis is applied to the hmF2 dataset which decomposed the dataset into a series of orthogonal functions (EOF base functions) E k and their associated EOF coefficients P k . The base functions E k represent the intrinsic characteristic variations of the dataset with the modified dip latitude and local time, the coefficients P k represents the variations of the dataset with the universal time, season as well as solar cycle activity levels. The second layer EOF analysis is applied to the EOF coefficients P k obtained in the first layer EOF analysis. The coefficients A k , obtained in the second layer EOF analysis, are then modelled with the harmonic functions representing the seasonal (annual and semi-annual) and solar cycle variations, with their amplitudes changing with the F 10.7 index, a proxy of the solar activity level. Thus, the constructed global model incorporates the geographical location, diurnal, seasonal as well as solar cycle variations of hmF2 through the combination of EOF analysis and the harmonic function expressions of the associated EOF coefficients. Comparisons between the model results and observational data were consistent, indicating that the modelling Zhang (zhangml@mail.iggcas.ac.cn) technique used is very promising when used to construct the global model of hmF2 and it has the potential of being used for the global modelling/mapping of other ionospheric parameters. Statistical analysis on model-data comparison showed that our constructed model of hmF2, based on the EOF expansion method, compares better with the observational data than the model currently used in the International Reference Ionosphere (IRI) model.

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An empirical orthogonal function model of total electron content over China

Cited by 72 publications

References 34 publications

Effects of typhoon Matsa on ionospheric TEC

Effects of typhoon Matsa on ionospheric TEC

Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis

A global model of the ionospheric F2 peak height based on EOF analysis

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An empirical orthogonal function model of total electron content over China

Cited by 72 publications

References 34 publications

Effects of typhoon Matsa on ionospheric TEC

Effects of typhoon Matsa on ionospheric TEC

Modeling ionospheric &lt;I&gt;fo&lt;/I&gt;F2 by using empirical orthogonal function analysis

A global model of the ionospheric F2 peak height based on EOF analysis

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Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis