A new model for ionospheric total electron content: the impact of solar flux proxies and indices

Abstract

We present a new high resolution empirical model for the ionospheric total electron content (TEC). TEC data are obtained from the global navigation satellite system (GNSS) receivers with a 1 o x 1 o spatial resolution and 5 minute temporal resolution. The linear regression model is developed at 45 o N, 0 o E for the years 2000 -2019 with 30 minute temporal resolution, unprecedented for typical empirical ionospheric models. The model describes dependency of TEC on solar flux, season, geomagnetic activity, and local time. Parameters describing solar and geomagnetic activity are evaluated. In particular, several options for solar flux input to the model are compared, including the 10.7cm solar radio flux (F 10.7 ), the Mg II core-to-wing ratio, and formulations of the solar extreme ultraviolet flux (EUV). Ultimately, the extreme ultraviolet flux presented by the Flare Irradiance Spectral Model, integrated from 0.05 to 105.05 nm, best represents the solar flux input to the model. TEC time delays to this solar parameter on the order of several days as well as seasonal modulation of the solar flux terms are included. The Ap 3 index and its history are used to reflect the influence of geomagnetic activity. The root mean squared error of the model (relative to the mean TEC observed in the 30-min window) is 1.9539 TECu. A validation of this model for the first three months of 2020 shows excellent agreement with data. The new model shows significant improvement over the

Generation of Proxy GIM‐TEC for Extreme Storms Before the Era of GNSS Observations

“…There are two main approaches to forecasting TEC. On the one hand, several straightforward models for direct forecasting of TEC are based on its interrelations with the driving forces (Goncharenko et al., 2021; Lean, 2019; Lean et al., 2016) or on the previous GIM evolution (Monte Moreno et al., 2018); a second approach is based on preliminary forecasting of ionosphere variability, which is then converted to GIM‐TEC calculations (Mukhtarov, Pancheva, et al., 2013). The latter approach is pursued in the present study.…”

“…Currently, climate models of the ionosphere and methods for forecasting the ionospheric disturbances are being successfully developed based on the analysis of TEC and Global Ionosphere Maps (GIM‐TEC) from GNSS observations during 23–24 solar cycles (SC). Relevant examples and references are presented in the works (Aa et al., 2012; Chen et al., 2015; Erdogan et al., 2020; Feng et al., 2019; Goncharenko et al., 2021; Gulyaeva, 1999; Ivanov et al., 2011; Jakowski et al., 2011; Lean, 2019; Lean et al., 2016; Q. Liu, Hernández‐Pajares, Lyu, Nishioka, et al., 2021; Meng & Verkhoglyadova, 2021; Mukhtarov, Andonov, et al., 2013; Mukhtarov, Pancheva, et al., 2013; Ratovsky et al., 2020). The machine learning and artificial intelligence models are applied by Cesaroni et al.…”