Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis
A similar-parameters interpolation method and an empirical orthogonal function analysis are used to construct empirical models for the ionospheric <I>fo</I>F2 by using the observational data from three ground-based ionosonde stations in Japan which are Wakkanai (Geographic 45.4° N, 1...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2011-08-01
|
| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/29/1501/2011/angeo-29-1501-2011.pdf |
| _version_ | 1818130258372591616 |
|---|---|
| author | E. A E. A D.-H. Zhang D.-H. Zhang Z. Xiao Z. Xiao Y.-Q. Hao A. J. Ridley M. Moldwin |
| author_facet | E. A E. A D.-H. Zhang D.-H. Zhang Z. Xiao Z. Xiao Y.-Q. Hao A. J. Ridley M. Moldwin |
| author_sort | E. A |
| collection | DOAJ |
| description | A similar-parameters interpolation method and an empirical orthogonal
function analysis are used to construct empirical models for the ionospheric
<I>fo</I>F2 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
1971–1987. The impact of different drivers towards ionospheric <I>fo</I>F2 can be
well indicated by choosing appropriate proxies. It is shown that the missing
data of original <I>fo</I>F2 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 <I>fo</I>F2 while the coefficients represent the
long-term alteration tendency. The 1st order EOF coefficient <I>A</I><sub>1</sub> can
reflect the feature of the components with solar cycle variation. <I>A</I><sub>1</sub>
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 <I>A</I><sub>2</sub>
contains mainly annual variation components. The 3rd order coefficient
<I>A</I><sub>3</sub> and 4th order coefficient <I>A</I><sub>4</sub> 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 <I>A</I><sub>4</sub>. 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 characteristics 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. |
| first_indexed | 2024-12-11T08:02:11Z |
| format | Article |
| id | doaj.art-89671a7364314c11a540d507e497e64c |
| institution | Directory Open Access Journal |
| issn | 0992-7689 1432-0576 |
| language | English |
| last_indexed | 2024-12-11T08:02:11Z |
| publishDate | 2011-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Annales Geophysicae |
| spelling | doaj.art-89671a7364314c11a540d507e497e64c2022-12-22T01:15:06ZengCopernicus PublicationsAnnales Geophysicae0992-76891432-05762011-08-01291501151510.5194/angeo-29-1501-2011Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysisE. A0E. A1D.-H. Zhang2D.-H. Zhang3Z. Xiao4Z. Xiao5Y.-Q. Hao6A. J. Ridley7M. Moldwin8Institute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, ChinaDepartment of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USAInstitute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, ChinaState Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing, ChinaInstitute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, ChinaState Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing, ChinaInstitute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, ChinaDepartment of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USADepartment of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USAA similar-parameters interpolation method and an empirical orthogonal function analysis are used to construct empirical models for the ionospheric <I>fo</I>F2 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 1971–1987. The impact of different drivers towards ionospheric <I>fo</I>F2 can be well indicated by choosing appropriate proxies. It is shown that the missing data of original <I>fo</I>F2 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 <I>fo</I>F2 while the coefficients represent the long-term alteration tendency. The 1st order EOF coefficient <I>A</I><sub>1</sub> can reflect the feature of the components with solar cycle variation. <I>A</I><sub>1</sub> 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 <I>A</I><sub>2</sub> contains mainly annual variation components. The 3rd order coefficient <I>A</I><sub>3</sub> and 4th order coefficient <I>A</I><sub>4</sub> 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 <I>A</I><sub>4</sub>. 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 characteristics 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.https://www.ann-geophys.net/29/1501/2011/angeo-29-1501-2011.pdf |
| spellingShingle | E. A E. A D.-H. Zhang D.-H. Zhang Z. Xiao Z. Xiao Y.-Q. Hao A. J. Ridley M. Moldwin Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis Annales Geophysicae |
| title | Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis |
| title_full | Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis |
| title_fullStr | Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis |
| title_full_unstemmed | Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis |
| title_short | Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis |
| title_sort | modeling ionospheric i fo i f2 by using empirical orthogonal function analysis |
| url | https://www.ann-geophys.net/29/1501/2011/angeo-29-1501-2011.pdf |
| work_keys_str_mv | AT ea modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT ea modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT dhzhang modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT dhzhang modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT zxiao modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT zxiao modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT yqhao modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT ajridley modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis AT mmoldwin modelingionosphericifoif2byusingempiricalorthogonalfunctionanalysis |