A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay
Globally distributed GPS (global positioning system) stations have been continuously running for nearly 20 years, thereby accumulating numerous observations. These long-time recorded GPS data can be used to calculate continuous total electron content (TEC) values at single stations and provide an ef...
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ftdoajarticles:oai:doaj.org/article:572600daa7e24fc88dfdb6632929e676 2023-05-15T13:58:09+02:00 A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay Zhao Zhenzhen Feng Jiandi Han Baomin Wang Zhengtao 2018-01-01T00:00:00Z https://doi.org/10.1051/swsc/2018047 https://doaj.org/article/572600daa7e24fc88dfdb6632929e676 EN eng EDP Sciences https://doi.org/10.1051/swsc/2018047 https://doaj.org/toc/2115-7251 2115-7251 doi:10.1051/swsc/2018047 https://doaj.org/article/572600daa7e24fc88dfdb6632929e676 Journal of Space Weather and Space Climate, Vol 8, p A59 (2018) empirical TEC models ionospheric delay single station GPS data Meteorology. Climatology QC851-999 article 2018 ftdoajarticles https://doi.org/10.1051/swsc/2018047 2022-12-31T07:48:17Z Globally distributed GPS (global positioning system) stations have been continuously running for nearly 20 years, thereby accumulating numerous observations. These long-time recorded GPS data can be used to calculate continuous total electron content (TEC) values at single stations and provide an effective modeling dataset to establish single-station empirical TEC models. In this paper, a new empirical TEC model called SSM-T1 for single stations is proposed on the basis of GPS data calculated by IONOLAB-TEC application from 2004 to 2015. The SSM-T1 model consists of three parts: diurnal, seasonal, and solar dependency variations, with 18 coefficients fitted by the nonlinear least-squares method. The SSM-T1 model is tested at four stations: Paris (opmt), France; Bangalore (iisc), India; Ceduna (cedu), Australia; and O’Higgins (ohi3) over the Antarctic Peninsula. The RMS values of the model residuals at these four stations are 3.22, 4.46, 3.28, and 3.83 TECU. Assessment results show that the SSM-T1 model is in good agreement with the observed GPS-TEC data and exhibits good prediction ability at the Paris, Bangalore, and Ceduna stations. However, at the O’Higgins station, the SSM-T1 model seriously deviates from the observed GPS-TEC data and cannot effectively describe the variation of mid-latitude summer night anomaly. Article in Journal/Newspaper Antarc* Antarctic Antarctic Peninsula Directory of Open Access Journals: DOAJ Articles Antarctic Antarctic Peninsula The Antarctic Journal of Space Weather and Space Climate 8 A59 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
empirical TEC models ionospheric delay single station GPS data Meteorology. Climatology QC851-999 |
spellingShingle |
empirical TEC models ionospheric delay single station GPS data Meteorology. Climatology QC851-999 Zhao Zhenzhen Feng Jiandi Han Baomin Wang Zhengtao A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
topic_facet |
empirical TEC models ionospheric delay single station GPS data Meteorology. Climatology QC851-999 |
description |
Globally distributed GPS (global positioning system) stations have been continuously running for nearly 20 years, thereby accumulating numerous observations. These long-time recorded GPS data can be used to calculate continuous total electron content (TEC) values at single stations and provide an effective modeling dataset to establish single-station empirical TEC models. In this paper, a new empirical TEC model called SSM-T1 for single stations is proposed on the basis of GPS data calculated by IONOLAB-TEC application from 2004 to 2015. The SSM-T1 model consists of three parts: diurnal, seasonal, and solar dependency variations, with 18 coefficients fitted by the nonlinear least-squares method. The SSM-T1 model is tested at four stations: Paris (opmt), France; Bangalore (iisc), India; Ceduna (cedu), Australia; and O’Higgins (ohi3) over the Antarctic Peninsula. The RMS values of the model residuals at these four stations are 3.22, 4.46, 3.28, and 3.83 TECU. Assessment results show that the SSM-T1 model is in good agreement with the observed GPS-TEC data and exhibits good prediction ability at the Paris, Bangalore, and Ceduna stations. However, at the O’Higgins station, the SSM-T1 model seriously deviates from the observed GPS-TEC data and cannot effectively describe the variation of mid-latitude summer night anomaly. |
format |
Article in Journal/Newspaper |
author |
Zhao Zhenzhen Feng Jiandi Han Baomin Wang Zhengtao |
author_facet |
Zhao Zhenzhen Feng Jiandi Han Baomin Wang Zhengtao |
author_sort |
Zhao Zhenzhen |
title |
A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
title_short |
A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
title_full |
A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
title_fullStr |
A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
title_full_unstemmed |
A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay |
title_sort |
single-station empirical tec model based on long-time recorded gps data for estimating ionospheric delay |
publisher |
EDP Sciences |
publishDate |
2018 |
url |
https://doi.org/10.1051/swsc/2018047 https://doaj.org/article/572600daa7e24fc88dfdb6632929e676 |
geographic |
Antarctic Antarctic Peninsula The Antarctic |
geographic_facet |
Antarctic Antarctic Peninsula The Antarctic |
genre |
Antarc* Antarctic Antarctic Peninsula |
genre_facet |
Antarc* Antarctic Antarctic Peninsula |
op_source |
Journal of Space Weather and Space Climate, Vol 8, p A59 (2018) |
op_relation |
https://doi.org/10.1051/swsc/2018047 https://doaj.org/toc/2115-7251 2115-7251 doi:10.1051/swsc/2018047 https://doaj.org/article/572600daa7e24fc88dfdb6632929e676 |
op_doi |
https://doi.org/10.1051/swsc/2018047 |
container_title |
Journal of Space Weather and Space Climate |
container_volume |
8 |
container_start_page |
A59 |
_version_ |
1766266247113080832 |