Performance of the double-thin-shell approach for studying nighttime mediumscale traveling ionospheric disturbances using two dense GNSS observation networks in Japan

Abstract

Electrodynamic coupling between the ionospheric E and F regions is widely recognized as the underlying mechanism for generating medium-scale traveling ionospheric disturbances (MSTIDs) during nighttime at midlatitudes. Recently, the double-thin-shell approach has proven to be a useful tool for studying the E-F coupling. By using total electron content (TEC) measurements, this approach enables the simultaneous reconstruction of electron density perturbations in both the E and F regions with broad and continuous coverage. However, the current reconstruction performance is limited when using only GPS-TEC measurements from GEONET, a dense network of groundbased Global Navigation Satellite System (GNSS) receivers over Japan. The expansion of available data sources and the integration of multi-GNSS observation data are considered important to enhance the double-thin-shell model. Fortunately, SoftBank Corp., a Japanese telecommunications provider, has recently developed a dense independent GNSS observation network to improve positioning services. In this paper, we analyze the potential of the improved double-thin-shell approach and emphasize the importance of incorporating multi-GNSS observation data from both GEONET and SoftBank networks. The solvability analysis, simulation, and observation results collectively indicate a substantial improvement in the spatiotemporal resolution. Specifically, the longitudinal and latitudinal resolution is improved from 0.15∘ to 0.1∘ in the E region, and from 0.5∘ to 0.3∘ in the F region. The temporal resolution is also improved from 2 minutes to 1 minute. In addition, significant improvements have been achieved in the reconstruction performance, particularly for the E region under complex background conditions. Based on these assessments, we conclude that the incorporation of GEONET and SoftBank GNSS observation data holds significant potential for improving the double-thin-shell model and advancing our understanding of MSTIDs.