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Three‐dimensional ionospheric tomography reconstruction using the model function approach in Tikhonov regularization
Author(s) -
Wang Sicheng,
Huang Sixun,
Xiang Jie,
Fang Hanxian,
Feng Jian,
Wang Yu
Publication year - 2016
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/2016ja023487
Subject(s) - tikhonov regularization , gnss applications , total electron content , regularization (linguistics) , covariance , tomography , satellite , covariance function , algorithm , inverse problem , remote sensing , mathematics , computer science , geodesy , ionosphere , physics , statistics , mathematical analysis , geology , tec , artificial intelligence , geophysics , optics , astronomy
Abstract Ionospheric tomography is based on the observed slant total electron content (sTEC) along different satellite‐receiver rays to reconstruct the three‐dimensional electron density distributions. Due to incomplete measurements provided by the satellite‐receiver geometry, it is a typical ill‐posed problem, and how to overcome the ill‐posedness is still a crucial content of research. In this paper, Tikhonov regularization method is used and the model function approach is applied to determine the optimal regularization parameter. This algorithm not only balances the weights between sTEC observations and background electron density field but also converges globally and rapidly. The background error covariance is given by multiplying background model variance and location‐dependent spatial correlation, and the correlation model is developed by using sample statistics from an ensemble of the International Reference Ionosphere 2012 (IRI2012) model outputs. The Global Navigation Satellite System (GNSS) observations in China are used to present the reconstruction results, and measurements from two ionosondes are used to make independent validations. Both the test cases using artificial sTEC observations and actual GNSS sTEC measurements show that the regularization method can effectively improve the background model outputs.