[1] We investigate the extreme ionospheric effect during the intense solar flare (X17.0/3B) that occurred on 7 September 2005. A strong E region electron density enhancement is observed by the incoherent scatter radars at Millstone Hill, Sondrestrom, and Tromsø, as well as by the radio occultation experiment on board the CHAMP satellite. The observations from both Millstone Hill and Sondrestrom stations show the average percentage enhancements of electron density during 17:40–18:10 UT are more than 200% near the E region peak height but only about 10% near the F region peak height; as a result, it leads to an unusual phenomenon that the E region electron density exceeds the F region electron density. We ascribe the unusual response to weak enhancement in EUV flux and strong enhancement in X‐ray flux during this flare. To further understand this unusual feature, we analyze in detail the E region response by comparing the electron production rates derived from the measurements with those fitted by the Chapman production function. Our results demonstrate that the Chapman production theory fits the observations better in the flare time than in the nonflare time, which is attributed to the obvious difference in the solar radiation spectra at flare and nonflare times. Owing to the strong enhancement in X‐ray flux during this flare, the E region electron production is more dominated by the X‐ray, and the Chapman ionization theory is more applicable in the flare time than in the nonflare time. In addition, we propose a method to estimate the effective solar radiation flux from the ionospheric observations of electron density profiles. The radiation flux derived with our method agrees well with the X‐ray flux at 0.1–0.8 nm observed by GOES‐12.

The application of deep learning in high‐precision ionospheric parameter prediction has become one of the focus in space weather research. In this study, an improved model called Mixed Convolutional Neural Networks (CNN)—Bi‐Long Short Term Memory is proposed for predicting future ionospheric Total Electron Content (TEC). The model is trained using the longest available (25 years) Global Ionospheric Maps‐TEC and evaluated the accuracy of ionospheric storm predictions. The results indicate that using historical TEC in the solar‐geographical reference frame as input driving data achieves higher prediction accuracy compared to that in the geocentric coordinate system. Additionally, by comparing different input parameters, it is found that incorporating the Kp, ap, and Dst indices as inputs to the model effectively improves its accuracy, especially in long‐term forecasting where R² increased by 3.49% and Root Mean Square Error decreased by 13.48%. Compared with BiLSTM‐Deep Neural Networks (DNN) and CNN‐BiLSTM, the Mixed CNN‐BiLSTM model has the highest prediction accuracy. It suggests that the utilization of CNN modules for processing spatial information, along with the incorporation of DNN modules to incorporate geomagnetic indices for result correction. Moreover, in short‐term predictions, the model accurately forecasts the evolution process of ionospheric storms. When extending the predicted length, although there are cases of prediction errors, the model still captures the entire process of ionospheric storms. Furthermore, the predicted results are significantly influenced by longitude, magnetic latitude, and local time.

According to the Chapman ionization theory, an ionospheric solar flare activity indicator (ISFAI) is given by the solar zenith angle and the variation rate of ionospheric vertical total electron content, which is measured from a global network of dual‐frequency GPS receivers. The ISFAI is utilized to statistically analyze the ionospheric responses to 1439 M‐class and 126 X‐class solar flares during solar cycle 23 (1996–2008). The statistical results show that the occurrence of ISFAI peak increases obviously at 3.2 total electron content unit (TECU)/h (1 TECU = 10¹⁶ el m⁻²) and reaches the maximum at 10 TECU/h during M‐class flares and 10 TECU/h and 40 TECU/h for X‐class flares. ISFAI is closely correlated with the 26–34 nm extreme ultraviolet flux but poorly related to the 0.1–0.8 nm X‐ray flux. The central meridian distance (CMD) of flare location is an important reason for depressing relationship between ISFAI and X‐ray Flux. Through the CMD effect modification, the ISFAI has a significant dependence on the X‐ray flux with a correlation coefficient of 0.76. The ISFAI sensitivity enables to detect the extreme X‐class flares, as well as the variations of one order of magnitude or even smaller (such as for C‐class flares). Meanwhile, ISFAI is helpful to the calibration of the X‐ray flux at 0.1–0.8 nm observed by GOES during some flares. In addition, the statistical results demonstrate that ISFAI can detect 80% of all M‐class flares and 92% for all X‐class ones during 1996–2008.