Global gridded ionospheric electron density derivation during 2006-2016 by assimilating COSMIC TEC and its validation

• Published in: Journal of Geophysical Research: Space Physics
• Other Authors: He, Jianhui (author), Yue, Xinan (author), Astafyeva, Elvira (author), Le, Huijun (author), Ren, Zhipeng (author), Pedatella, Nicholas M. (author), Ding, Feng (author), Wei, Yong (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Langmuir; Weddell; Weddell Sea
• Online Access: https://doi.org/10.1029/2022JA030955

The long-term accurate specification of the Earth's ionospheric states is crucial to scientific research and applications in the space weather community. In the current work, a global monthly mean three-dimensional (3-D) ionospheric electron density product has been obtained during one solar cycle from 2006 to 2016. Specifically, an accurate 3-D product is reconstructed monthly by assimilating the Constellation Observing System for Meteorology, Ionosphere and Climate slant total electron content (TEC) into an empirical background model via the Kalman filter data assimilation algorithm. The outputs of the results have spatial resolutions of 2 degrees in latitude, 5 degrees in longitude, and 20 km in height, and temporal resolution of 1 hr in universal time. The accuracy and reliability of the results are systematically validated by the critical frequency at the F2 layer from global ionosonde stations, the in situ electron density from the CHAllenging Minisatellite Payload Planar Langmuir Probe, the TEC from the Massachusetts Institute of Technology, and Gravity Recovery and Climate Experiment. We found that the products agree well with the independent observations. Some well-known ionospheric climatological patterns, including the solar and seasonal variation, annual asymmetry, the Weddell Sea Anomaly, and the longitudinal wave structure, can be well illustrated. The advantages of the products are its 3-D and gridded electron density and continuous one solar cycle time series (2006-2016). It is useful to study the spatial-temporal variations in ionospheric states from seasons to decades, which can also be used as the background parameters for atmospheric and ionospheric-related scientific research and applications.