The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 10⁴ el/cm³. Under quiet space weather, the predicted accuracy of the ANN‐TDD is 30%–60% higher than the IRI‐2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN‐TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI‐2016 with the STORM option activated. Additionally, the ANN‐TDD successfully reproduces the large‐scale horizontal‐vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC‐2 mission. Furthermore, the ANN‐TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid‐latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved.

中文翻译：

---

多层人工神经网络在COSMIC，风云3C和Digisonde的长期观测中在3D全球电子密度模型中的应用

电离层在卫星导航，无线电通信和太空天气预报中起着重要作用。但是，开发具有高可预测性的模型以捕获电离层电动力学的水平-垂直特征仍然是一项艰巨的任务。在这项研究中，利用2005-2019年期间从星载全球导航卫星系统（GNSS）无线电掩星（RO）系统（COSMIC和FY-3C）和Digisonde全球电离层无线电天文台获得的多个观测数据，开发了一个完整的全球电离层3基于人工神经网络的三维电子密度模型，即ANN-TDD。对于训练，验证和测试数据集，预测轮廓的相关系数都超过0.96，并且预测残差的最小均方根误差为7.8×10⁴ el / cm ³。在安静的太空天气下，ANN‐TDD的预测精度比Millstone Hill和Jicamarca非相干散射雷达的IRI‐2016高30％–60％。但是，与激活了STORM选件的IRI-2016相比，ANN-TDD在强地磁风暴下无法预测电离层动态演变。此外，ANN-TDD成功地再现了大规模的水平-垂直电离层电动力特征，包括季节变化和半球不对称性。这些功能与从FORMOSAT / COSMIC-2任务获得的RO剖面所揭示的结构非常吻合。此外，ANN-TDD成功捕获了主要的区域电离层模式，包括赤道电离异常，韦德尔海异常和中纬度夏季夜间异常。