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• W2750611844 abstract "Ionospheric tomography has been widely employed in imaging the large-scale ionospheric structures at both quiet and storm times. However, the tomographic algorithms to date have not been very effective in imaging of medium- and small-scale ionospheric structures due to limitations of uneven ground-based data distributions and the algorithm itself. Further, the effect of the density and quantity of Global Navigation Satellite Systems data that could help improve the tomographic results for the certain algorithm remains unclear in much of the literature. In this paper, a new multipass tomographic algorithm is proposed to conduct the inversion using intensive ground GPS observation data and is demonstrated over the U.S. West Coast during the period of 16-18 March 2015 which includes an ionospheric storm period. The characteristics of the multipass inversion algorithm are analyzed by comparing tomographic results with independent ionosonde data and Center for Orbit Determination in Europe total electron content estimates. Then, several ground data sets with different data distributions are grouped from the same data source in order to investigate the impact of the density of ground stations on ionospheric tomography results. Finally, it is concluded that the multipass inversion approach offers an improvement. The ground data density can affect tomographic results but only offers improvements up to a density of around one receiver every 150 to 200 km. When only GPS satellites are tracked there is no clear advantage in increasing the density of receivers beyond this level, although this may change if multiple constellations are monitored from each receiving station in the future." @default.
• W2750611844 created "2017-09-15" @default.
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• W2750611844 date "2017-06-01" @default.
• W2750611844 modified "2023-10-10" @default.
• W2750611844 title "A multiresolution inversion for imaging the ionosphere" @default.
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• W2750611844 doi "https://doi.org/10.1002/2016ja023728" @default.
• W2750611844 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6049895" @default.
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