The inhomogeneous distribution of Global Navigation Satellite System (GNSS) stations results in inaccurate vertical total electron contents (VTECs) in global ionosphere maps (GIMs) over areas with large GNSS data gaps. Incorporating VTECs from the International Reference Ionosphere (IRI) model is usually adopted as one approach to mitigate the inaccurate VTECs. However, large and complicated spatiotemporal varying VTEC biases between GNSS and IRI suggest a robust strategy to optimally combine GNSS and IRI VTECs for operational high-precision modeling. Here, we thoroughly analyze the characteristics of VTEC biases between GNSS and IRI-2016 model in different latitudes from 2009 to 2019, and develop an improved functional and stochastic model. An automated assimilation strategy of GNSS and IRI-2016 VTECs is proposed for Shanghai Astronomical Observatory final GIM (SHAG) routine estimation, and the reliability of GIMs in areas with lack of stations is enhanced by attaching Virtual Observation Stations (VOSs) based on IRI-2016 model and VOS bias parameters. Experimental results show that the root-mean-square errors (RMSEs) of SHAG with respect to VTECs retrieved from four independent GNSS assessment stations are reduced by 21.65–53.06% in the large data gaps with the assistance of VOSs. Furthermore, we validated the long-term reliability of SHAG spanned one solar cycle (2009–2019) with International GNSS Service (IGS) final GIMs and satellite altimetry VTECs. Validation results suggest that SHAG is in good agreement with IGS final GIMs, and reliability of SHAG in large GNSS data gap areas is significantly improved by attaching VOSs and biases. This methodology also represents an efficient tool for automated global ionospheric modeling integrating multi-source data.

全球导航卫星系统 (GNSS) 站的不均匀分布导致全球电离层地图 (GIM) 中的垂直总电子含量 (VTECs) 在 GNSS 数据缺口较大的地区不准确。结合国际参考电离层 (IRI) 模型中的 VTECs 通常被用作减轻不准确 VTECs 的一种方法。然而，GNSS 和 IRI 之间的大而复杂的时空变化 VTEC 偏差表明了一种稳健的策略，可以将 GNSS 和 IRI VTEC 最佳组合以进行操作高精度建模。在这里，我们深入分析了 2009 年至 2019 年不同纬度的 GNSS 和 IRI-2016 模型之间的 VTEC 偏差特征，并开发了改进的函数和随机模型。针对上海天文台最终GIM（SHAG）例行估算，提出了GNSS和IRI-2016 VTECs的自动同化策略，并通过附加基于IRI- 2016 模型和 VOS 偏差参数。实验结果表明，在 VOS 的帮助下，在大数据间隙中，SHAG 相对于从四个独立 GNSS 评估站检索到的 VTEC 的均方根误差（RMSE）降低了 21.65-53.06%。此外，我们使用国际 GNSS 服务 (IGS) 最终 GIM 和卫星测高 VTEC 验证了 SHAG 跨越一个太阳周期（2009-2019 年）的长期可靠性。验证结果表明 SHAG 与 IGS 最终 GIM 非常一致，通过附加 VOS 和偏差，SHAG 在大型 GNSS 数据差距区域的可靠性显着提高。该方法还代表了一种集成多源数据的自动化全球电离层建模的有效工具。