The increasing amount of the Global Navigation Satellite System (GNSS) ground receivers provides extremely abundant data resource to ionospheric community. In present paper, a hybrid data assimilation method is proposed to derive the global ionosphere maps (GIM) using the GNSS measurements. This method can balance the weights of background information from the International Reference Ionosphere model (IRI‐2016) and actual observations to reach a reasonable estimation, and its background error covariance (BEC) is a weighted linear combination of climatological static BEC and ensemble‐based flow‐dependent BEC. The results show that this method is capable of improving the background outputs significantly. To evaluate the quantitative contribution of background information on the estimated GIM, the background influence analysis is performed. The background information contributes less in the data‐rich areas and more in the data‐sparse areas. The mean background influence is up to 0.93 for the reconstructed GIM; that is, about 93% of the contribution is due to the background information, and the complementary 7% is the influence of the assimilated observations.

中文翻译：

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混合数据同化方法估算全球电离层图及其背景影响分析

全球导航卫星系统（GNSS）地面接收器的数量不断增加，为电离层社区提供了极为丰富的数据资源。在本文中，提出了一种混合数据同化方法，以使用GNSS测量值导出全球电离层图（GIM）。该方法可以平衡国际参考电离层模型（IRI‐2016）和实际观测值的背景信息权重，以得出合理的估计值，其背景误差协方差（BEC）是气候静态BEC和基于集合的加权线性组合流量相关的BEC。结果表明，该方法能够显着提高背景输出。为了评估背景信息对估计的GIM的定量贡献，执行了背景影响分析。背景信息在数据丰富的区域贡献较少，而在数据稀疏的区域贡献较大。重建的GIM的平均背景影响高达0.93；也就是说，约93％的贡献是由于背景信息造成的，而互补的7％是受同化观测的影响。