The Total Electron Content (TEC) computed from ionospheric models is a widely used parameter for characterizing the morphological structure of the ionosphere. The global TEC maps from empirical models, like the International Reference Ionosphere (IRI) model, have limited accuracy compared to those calculated by dual-frequency measurements from the global navigation satellite systems (GNSS). We have developed a reconstructed IRI TEC model for generating high-precision global TEC maps based on a deep learning method. For this, we have collected 48,204 pairs of global TEC maps from the IRI-2020 model and Global Ionosphere Maps (GIM) model with 2-h time resolution from 2009 to 2019 covering the whole solar cycle 24. The daily solar radio flux (F_10.7), sunspot number (SSN), Dst, and Kp indices are also introduced as input features to train the model. We have investigated the optimum combination of the input parameters for the reconstructed TEC model and compared the performance of the model during the years with high and low solar activity levels. Results show that the reconstructed TEC model with F_10.7 and Kp features has a better performance compared to that considering all solar and geomagnetic indices. The global TEC maps predicted from our model are much more consistent with the corresponding TEC maps from the GIM model than those from the IRI-2020 model. Especially, the large-scale equatorial ionospheric anomaly (EIA) crests and the pronounced enhancement of TEC are well predicted by the reconstructed TEC model. From statistical metrics, the accuracy of the reconstructed TEC model increased by 40.8% during the high solar activity year 2015 and 43.0% during the low solar activity year 2018 compared with the IRI-2020 model. The prediction performance of the reconstructed TEC model also shows better accuracy during the storm periods.

根据电离层模型计算得出的总电子含量 (TEC) 是广泛用于表征电离层形态结构的参数。与通过全球导航卫星系统 (GNSS) 双频测量计算得出的地图相比，来自国际参考电离层 (IRI) 模型等经验模型的全球 TEC 地图的精度有限。我们开发了一个重建的 IRI TEC 模型，用于基于深度学习方法生成高精度的全球 TEC 地图。为此，我们从 2009 年至 2019 年的 IRI-2020 模型和全球电离层图 (GIM) 模型中收集了 48,204 对 2 小时时间分辨率的全球 TEC 地图，涵盖了整个太阳活动周期 24。每日太阳射电通量 ( F _10.7）、太阳黑子数（SSN）、Dst 和K p 指数也被引入作为输入特征来训练模型。我们研究了重建 TEC 模型的输入参数的最佳组合，并比较了太阳活动水平高和低的年份中模型的性能。结果表明，与考虑所有太阳和地磁指数的模型相比，具有F _10.7和K p 特征的重建TEC 模型具有更好的性能。与 IRI-2020 模型相比，我们的模型预测的全球 TEC 地图与 GIM 模型中相应的 TEC 地图更加一致。特别是，重建的TEC模型很好地预测了大范围的赤道电离层异常（EIA）波峰和TEC的显着增强。从统计指标来看，与IRI-2020模型相比，重建的TEC模型在2015年太阳活动高年和2018年太阳低活动年的精度分别提高了40.8%和43.0%。重建的 TEC 模型的预测性能在暴风雨期间也表现出更好的准确性。