The ionosphere's dynamic structure affects electromagnetic radiation by altering radio wave propagation, impacting daily communications. The characteristics of the ionosphere are primarily characterized by electron density parameters. This paper proposes a method to construct Three‐Dimensional (3‐D) electron density distributions with arbitrary spatiotemporal resolution in ISR observational regions. The method, termed Artificial Intelligence‐based data assimilation (AI‐Assim), integrates data assimilation directly into a neural network. It assimilates electron density from the IRI‐2020 model to fill ISR observation gaps. Experiments conducted using the Sanya Incoherent Scatter Radar (SYISR) in Hainan, China, successfully constructed a 3‐D electron density structure over the region, with a 0.2° latitude/longitude resolution and 1 km height resolution. The method's effectiveness was validated by calculating the mean square error and comparing the results with digisonde measurements.

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

Accurate forecasting of the ionospheric state is crucial for various applications including remote sensing and navigation. Total electron content (TEC) is an important ionospheric parameter to reflect ionospheric state. Consequently, there is a great interest in the prediction of TEC. In this study, we integrated the Informer deep learning algorithm and Principal Component Analysis (PCA), a dimensionality reduction technique, to achieve spatio‐temporal modeling for forecasting the global TEC maps. Our evaluation, based on test set data from 2015 to 2022, demonstrate that the PCA‐Informer model outperforms the IRI‐2016, standalone Informer, and PCA‐enhanced Long Short‐Term Memory (PCA‐LSTM) models in terms of accuracy with root mean squared error (RMSE) of 2.60 TECU and mean relative error (MRE) of 14.1%, and stability for predicting TEC maps for the subsequent 2 days. Two distinct periods (a geomagnetic quiet period and a strong storm period) in 2018 have been selected for evaluating the models' efficacy. The PCA‐Informer model has shown remarkable predictive precision during the initial and main phase of strong geomagnetic storms as well as quiet period. During 132 geomagnetic storm events on the test set, the PCA model exhibits RMSE of 3.6 TECU and MRE of 17.7%, which are lower than IRI‐2016 (6.1 TECU, 33.38%), PCA‐LSTM (4.5 TECU, 21.52%) and Informer (4.1 TECU, 20.64%). Additionally, model errors are negatively correlated with the minimum Dst, while PCA‐Informer has the best robustness.