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Regional ionospheric model response of geomagnetic storm during March 2015 using data fusion mechanism: GPS, COSMIC RO and SWARM

Emmela Suneetha, Ratnam Devanaboyina Venkata

Acta Geophysica

2023

Vol. 71, no. 1

553--566

The total electron content (TEC) maps are chosen as the elementary structures to provide ionospheric corrections for improving the positional accuracy for Global Navigational Satellite Systems (GNSS) users. Availability of total electron content data from a multi-constellation of satellite systems and various ground-based instruments possess an ability to monitor, nowcast and forecast the behavior of the ionosphere region. Conversely, combining ionospheric TEC data from different temporal and spatial scales is a difficult task to augment either ground or space-based ionospheric model's accuracy. And hence, a method like data fusion is essential to illustrate the ionospheric variability and to improve the accuracy of ionospheric models under equatorial ionization anomaly (EIA) conditions. This paper presented the weighted least square data fusion method with multi-instrument TEC data to analyze the EIA TEC structures in the low-latitude Indian region. Both ground-based (GPS TEC from 26 stations in the Indian region) and space-based (FORMOSAT-3/COSMIC RO and SWARM mini satellite constellation) observations are used for the analysis. The spherical harmonic function (SHF) model of order 2, which gives nine SHF coefficients, is implemented. The analysis illustrates that the SHF coefficients followed by TEC data fusion would be useful to investigate the entry, occupancy and exit TEC structures of EIA during geomagnetic storm conditions.

Support Vector Regression model to predict TEC for GNSS signals

Sivakrishna Kondaveeti, Ratnam Devanaboyina Venkata, Sivavaraprasad Gampala

Acta Geophysica

2022

Vol. 70, no 6

2827--2836

Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.