Estimating of the Global Ionosphere Maps Using Hybrid Data Assimilation Method and Their Background Influence Analysis.

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. Key Points: A hybrid data assimilation method is proposed to derive the GIM using the GNSS measurementsThe background influence analysis is performed to assess the quantitative contribution of background information on the estimated GIMThe sparse matrix technique is adopted to reduce the computational cost [ABSTRACT FROM AUTHOR]