全球电离层TEC经验模型研究是空间天气服务领域的重要课题之一。以IGS的TEC网格数据为背景建立的全球TEC经验模型近年来发展迅速，然而此类模型在海洋区域的预测精度较差，且对一些电离层异常现象的描述能力存在不足。针对以上两点问题，本项目旨在建立一种新的全球电离层TEC经验模型，拟融合多源TEC数据（CODE全球TEC网格数据、Jason测高卫星TEC数据、COSMIC掩星TEC数据和IRI2016模型TEC数据）作为建模数据集，以提高海洋区域的模型精度；研究给出全球TEC经验模型算法及子分量模型的函数表达式，重点顾及三种异常现象的函数模型研究，分别是MSNA异常、EIA异常和“磁滞效应”，使其能有效地描述和预测这三种异常现象。拟建立的全球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全球电离层经验模型具有重要的参考意义。

Study on the global ionospheric TEC empirical model is one of the most important topics in the field of space weather service. Global TEC empirical models based on TEC grid data provided by IGS (International GNSS Service) have been developed rapidly in recent years. However, the prediction accuracy of such models in ocean area is poor, and their description on some ionospheric anomalies is insufficient. In view of the above two problems, this project aims to establish a new global empirical TEC model. Four kinds of TEC data from CODE (Center for Orbit Determination in Europe), Jason altimetry satellite, COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) and IRI2016 model are integrated as the modeling data set to improve the model accuracy in ocean area. The model algorithm and functional expressions of the each component are studied, mainly focusing on the modeling of three kinds of anomalies (Mid-latitude Summer Nighttime Anomaly, Equatorial Ionization Anomaly and Hysteresis Effect), thereby making it effective to describe and predict these anomalies. The established global TEC empirical model can provide a new ionospheric delay correction method for GNSS single frequency users, and it is significant for building other new global ionospheric empirical models and improving the current global ionospheric empirical models.