Forecasting of global ionospheric TEC using a deep learning approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10291-023-01413-9.pdf
Reference45 articles.
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2. Béniguel Y (2002) Global ionospheric propagation model (GIM): a propagation model for scintillations of transmitted signals. Radio Sci 37(3):4-1-4–13. https://doi.org/10.1029/2000RS002393
3. Bent RB (1992) Bent ionospheric model (1972). Planet Space Sci 40:545–545
4. Bilitza D, Altadill D, Zhang Y, Mertens C, Truhlík V, Richards P, McKinnell L, Reinisch B (2014) The international reference ionosphere 2012 a model of international collaboration. J Space Weather Space Clim. https://doi.org/10.1051/swsc/2014004
5. Cesaroni C, Spogli L, Aragon-Angel A, Fiocca M, Dear V, Franceschi GD, Romano V (2019) Neural network based model for global total electron content forecasting. J Space Weather Space Clim 10:11. https://doi.org/10.1051/swsc/2020013
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