Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content
Abstract More and more satellite constellations and ground receivers are available all over the world, and they can provide rich ionospheric remote sensing data sources. Due to the fact that different analysis centers use different generation techniques and different ground stations, their routinely...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002675 |
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| author | Sicheng Wang Sixun Huang Hanxian Fang |
| author_facet | Sicheng Wang Sixun Huang Hanxian Fang |
| author_sort | Sicheng Wang |
| collection | DOAJ |
| description | Abstract More and more satellite constellations and ground receivers are available all over the world, and they can provide rich ionospheric remote sensing data sources. Due to the fact that different analysis centers use different generation techniques and different ground stations, their routinely released Global Ionosphere Maps (GIMs) have some differences. To study the problems of how many stations and which stations should be optimally chosen to generate the GIMs, a step‐by‐step iterative method based on the information content is proposed under the framework of 3DVAR data assimilation. This method adopts the information content to qualitatively evaluate the information contribution of a measurement to the reconstructed GIM, and selects the ground station with a maximum information content in every iteration. About 500 ground receivers from the International GNSS Services networks and Multi‐GNSS Experiment networks are collected from DOY 001 to DOY 120 in 2018 to demonstrate the feasibility of this method. The results show that the information contribution from the first 100 selected stations is over 70%, and the information contribution from the first 200 chosen stations is up to 87%. When the station selection criterion is taken as 95% of the total information content, nearly 300 stations are needed to reconstruct each GIM. Moreover, the quantitative information contribution from GPS, GLONASS, GALILEO, and BEIDOU satellite constellations is preliminarily analyzed. |
| format | Article |
| id | doaj-art-bef4c4290e764066b288eaaf799eb924 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-bef4c4290e764066b288eaaf799eb9242025-01-14T16:35:20ZengWileySpace Weather1542-73902022-01-01201n/an/a10.1029/2020SW002675Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information ContentSicheng Wang0Sixun Huang1Hanxian Fang2Institute of Meteorology and Oceanography National University of Defense Technology Changsha ChinaInstitute of Meteorology and Oceanography National University of Defense Technology Changsha ChinaInstitute of Meteorology and Oceanography National University of Defense Technology Changsha ChinaAbstract More and more satellite constellations and ground receivers are available all over the world, and they can provide rich ionospheric remote sensing data sources. Due to the fact that different analysis centers use different generation techniques and different ground stations, their routinely released Global Ionosphere Maps (GIMs) have some differences. To study the problems of how many stations and which stations should be optimally chosen to generate the GIMs, a step‐by‐step iterative method based on the information content is proposed under the framework of 3DVAR data assimilation. This method adopts the information content to qualitatively evaluate the information contribution of a measurement to the reconstructed GIM, and selects the ground station with a maximum information content in every iteration. About 500 ground receivers from the International GNSS Services networks and Multi‐GNSS Experiment networks are collected from DOY 001 to DOY 120 in 2018 to demonstrate the feasibility of this method. The results show that the information contribution from the first 100 selected stations is over 70%, and the information contribution from the first 200 chosen stations is up to 87%. When the station selection criterion is taken as 95% of the total information content, nearly 300 stations are needed to reconstruct each GIM. Moreover, the quantitative information contribution from GPS, GLONASS, GALILEO, and BEIDOU satellite constellations is preliminarily analyzed.https://doi.org/10.1029/2020SW002675Global Ionosphere Mapsstation selectioninformation contentdata assimilation |
| spellingShingle | Sicheng Wang Sixun Huang Hanxian Fang Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content Space Weather Global Ionosphere Maps station selection information content data assimilation |
| title | Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content |
| title_full | Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content |
| title_fullStr | Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content |
| title_full_unstemmed | Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content |
| title_short | Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content |
| title_sort | ground gnss station selection to generate the global ionosphere maps using the information content |
| topic | Global Ionosphere Maps station selection information content data assimilation |
| url | https://doi.org/10.1029/2020SW002675 |
| work_keys_str_mv | AT sichengwang groundgnssstationselectiontogeneratetheglobalionospheremapsusingtheinformationcontent AT sixunhuang groundgnssstationselectiontogeneratetheglobalionospheremapsusingtheinformationcontent AT hanxianfang groundgnssstationselectiontogeneratetheglobalionospheremapsusingtheinformationcontent |