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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Bibliographic Details
Published in:Journal of Space Weather and Space Climate
Main Authors: Zhao Zhenzhen, Feng Jiandi, Han Baomin, Wang Zhengtao
Format: Article in Journal/Newspaper
Language:English
Published: EDP Sciences 2018
Subjects:
Online Access:https://doi.org/10.1051/swsc/2018047
https://doaj.org/article/572600daa7e24fc88dfdb6632929e676
Description
Summary: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.