A New Artificial Neural Network‐Based Global Three‐Dimensional Ionospheric Model (ANNIM‐3D) Using Long‐Term Ionospheric Observations: Preliminary Results. Issue 6 (18th June 2019)

• Record Type: Journal Article
• Authors: Gowtam, V. Sai; Tulasi Ram, S.; Reinisch, B.; Prajapati, A.
• Is Part Of: Journal of geophysical research. Volume 124:Issue 6(2019)
• Page Start: 4639
• Page End: 4657
• Publication Date: 2019-06-18
• Subjects: ionosphere -- artificial neural networks -- modeling -- GPS‐radio occultation -- Digisonde; Magnetospheric physics -- Periodicals; Space environment -- Periodicals; Cosmic physics -- Periodicals; Planets -- Atmospheres -- Periodicals; Heliosphere (Astrophysics) -- Periodicals; Geophysics -- Periodicals; 523.01
• DOI: 10.1029/2019JA026540
• Languages: English
• ISSNs: 2169-9380

Abstract

Abstract: In this paper, we present the preliminary results of a new global three‐dimensional (3‐D) ionospheric model developed using artificial neural networks (ANNs) by assimilating long‐term ionospheric observations from nearly two decades of ground‐based Digisonde, satellite‐based topside sounders, and global positioning system‐radio occultation measurements. The present 3‐D model is named ANN‐based global 3‐D ionospheric model (ANNIM‐3D), which is the extension of previous work on the ANN‐based two‐dimensional ionospheric model by Sai Gowtam and Tulasi Ram (2017a, https://doi.org/10.1002/2017JA024795 ) and Tulasi Ram et al. (2018, https://doi.org/10.1029/2018JA025559 ). The vertical electron density profiles derived from ANNIM‐3D model are found to be consistent with the ground‐based incoherent scatter radar observations at Jicamarca and Millstone Hill. The model results have been thoroughly validated and found in good agreement with the ground‐based Digisonde and satellite in situ observations at different altitudes. This model successfully reproduces the large‐scale ionospheric phenomena like diurnal and seasonal variations of equatorial ionization anomaly and its hemispheric asymmetries, ionospheric annual anomaly, and the main ionospheric trough. Also, the present model has predicted the ionospheric response that is consistent with the neutral composition changes and meridional wind circulations during disturbed geomagnetic activity periods. Finally, the merits and limitations of this model and the scope for the potential improvements have been discussed. Key Points: A new artificial neural network‐based 3‐D ionospheric model (ANNIM‐3D) using nearly two decades of global ionospheric data is presented The ANNIM‐3D predictions are consistent with the ground‐based Digisonde, incoherent scatter radar, and satellite in situ observations The ANNIM‐3D can successfully reproduce EIA, annual anomaly, main ionospheric trough, and ionospheric response to geomagnetic activity