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Total electron content (TEC) is a key parameter in the mitigation of ionospheric effects on radio systems. This paper studied the performances of ionospheric TEC obtained from Centre National d'Etudes Spatiales (CNES) real-time ionospheric products. Taking the international global navigation satellite system service (IGS) global ionospheric map (GIM) as a reference, the deviation and root mean square of the TEC estimated by the CNES real-time TEC and NeQuick-G model were compared. Results showed that the CNES real-time ionospheric TEC and IGS GIM describe the global ionospheric temporal and spatial changes more closely. By using the empirical orthogonal function (EOF), the temporal and spatial characteristics of CNES real-time TEC and GIM TEC from day of year 230 to day of year 259 in 2021 were extracted and compared. The following main spatial variation patterns were extracted: the changes in geomagnetic latitude reflecting daily mean solar forcing and the changes in diurnal and semi-diurnal cycles. The time-varying characteristics of CNES real-time TEC and GIM TEC were compared by extracting the same EOF basis functions E 1 − E 5 . The EOF decomposition coefficients A 1 − A 5 reflect the strength of the spatial variation patterns, and they are all strongly correlated with solar activity. Among them, the intensity of diurnal cycle variations contained in CNES real-time TEC is in good agreement with GIM TEC, but the intensities of changes in geomagnetic latitude and semi-diurnal cycles are weaker than those of GIM TEC. [ABSTRACT FROM AUTHOR]

In this paper, the prediction model for ionospheric total electron content (TEC) based on Long Short-Term Memory (LSTM) deep learning network and its performance are discussed. The input parameters of the model are previous values of daily TEC, solar radio flux at 10.7 cm parameter of 81 day moving average ( F 107 _ 81 ‾ ), sunspot number (SSN), geomagnetic Kp index, and disturbance storm time (Dst) index, and the outputs are TEC values for the target day. TEC data from January 1, 2001 to December 31, 2016 were used in this study. The dataset almost covers most of the years of the last two solar cycles (23, 24), and it is separated as 81.3% for training, 6.2% for validation, and 12.5% for testing. At BJFS IGS station (39.61° N, 115.89° E), LSTM yielded good TEC estimates with an RMSE of 4.07 TECU in 2001, it was 33% and 48% lower than the RMSE observed in TEC prediction using BP and IRI-2016 models, respectively. In the year of low solar activity (2016), the RMSE predicted by LSTM was 1.78 TECU, it provided 30% and 54% lower RMSE for TEC prediction than for BP and IRI-2016 models. Under the condition of magnetic storm, the LSTM TEC predictions are more consistent with the corresponding IGS Global Ionospheric Maps (GIMs) TEC than TEC predictions by BP and IRI-2016 models. LSTM can better grasp the influence of different external conditions on TEC. Seventeen grid points along 120° E meridian in latitude range from 80° S to 80° N were selected to further study the performance of LSTM model in different latitude. Results show that the prediction accuracy of LSTM is better than that of BP at different latitudes, especially at low latitudes. The performances of the two models are highly correlated with latitude and solar activity, and are both better than that of IRI-2016. [ABSTRACT FROM AUTHOR]