本文利用中国地壳观测网络(陆态网络)GPS站点实测总电子含量(Total Electron Con-tent,TEC)数据,建立了京津冀区域电离层经验模型,并通过引入太阳通量和地磁活动数据来提高模型的性能.本文研究了电离层TEC的日变化、季节变化和地磁影响分量的函数模型,采用非线性最小二乘法拟合待定系数,提出了一种多参数经验融合模型——电离层TEC京津冀区域模型(Multi-Parameter Empirical Fusion Model-Ionospheric TEC Beijing-Tianjin-Hebei Region,MEFM-ITBTHR)——来预测京津冀区域电离层TEC.结果表明,MEFM-ITBTHR模型能够很好地拟合建模数据集.本文还对MEFM-ITBTHR模型的性能进行了地理位置变化分析、季节变化分析和地磁扰动分析.结果表明,在京津冀区域,MEFM-ITBTHR模型在不同经纬度、不同季节、不同地磁扰动下与实测TEC的预测效果、线性相关性、模型精度都优于IRI2020和NeQuick2模型.本文构建的区域TEC经验模型可为GNSS单频用户提供新的电离层延迟改正方法,同时对建立其他新的和改进现有的电离层经验模型具有重要的参考意义.
A Multi-Parameter Fusion Forecast Model of Ionospheric TEC in the Beijing-Tianjin-Hebei Region
This article utilizes the Total Electron Content(TEC)data measured by the GPS stations of the Chinese Crustal Observation Network(referred to as the"Crustal Net-work"hereafter)to establish an empirical ionospheric model for the Beijing-Tianjin-Hebei region.By incorporating solar flux and geomagnetic activity data,the performance of the model is enhanced.The study develops a functional model for the diurnal,seasonal varia-tion,and geomagnetic effect components of the ionospheric TEC,using a nonlinear least squares method to fit the coefficients.A multi-parameter empirical fusion model is pro-posed(Ionospheric TEC Beijing-Tianjin-Hebei Region Model,MEFM-ITBTHR)to pre-dict the ionospheric TEC in the Beijing-Tianjin-Hebei region.Results indicate that the MEFM-ITBTHR model fits the modeling dataset well.The performance of the MEFM-ITBTHR model is further analyzed through geographical variation,seasonal variation,and geomagnetic disturbance analysis.Results demonstrate that in the Beijing-Tianjin-Hebei region,the MEFM-ITBTHR model exhibits better forecasting accuracy,linear correla-tion,and model precision for measured TEC across different latitudes,seasons,and geo-magnetic disturbances compared to the IRI2020 and NeQuick2 models.The regional TEC empirical model constructed in this study provides a new method for ionospheric delay cor-rection for GNSS single-frequency users and holds significant reference value for establis-hing other new and improving existing empirical ionospheric models.
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