Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R² of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.

Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days.

Medium-scale traveling ionospheric disturbances (MSTIDs) to the June 22 and 23, 2015 geomagnetic storm over Japan are investigated by using the Global Navigation Satellite System Earth Observation Network of Japan (GEONET) in this letter. We have detected two groups of MSTIDs on June 22 and 23 and a negative ionospheric disturbance response on June 22 observed by the high-resolution detrended total electron content (TEC) maps from GPS observations. The negative ionospheric response (IR) performs a large-scale ionosphere depletion covering a wide range of areas (~125°E to ~140°E and ~25°N to ~40°N) simultaneously, which starts at 1900 universal time (UT) [04:00 local time (LT)] with a period of ~30 min on June 22. Compared with typical MSTIDs, the nighttime and the daytime MSTIDs in this letter both have a southwestward propagated direction and a propagated velocity of ~87.8 and ~157.8 m/s, respectively. The physical sources of the IRs over Japan during this geomagnetic storm are possibly attributed to the prompt penetration electric field (PPEF) and lower atmospheric gravity waves (AGW) referring to the previous studies. We believe that these findings can contribute to the complement of ionospheric research on MSTIDs over Japan.