Introducing Reflected GNSS TEC Data Into ANCHOR Ionospheric Data Assimilation Model

Abstract ANCHOR is a novel data assimilation (DA) algorithm developed at the U.S. Naval Research Laboratory to improve ionospheric nowcasting by increasing accuracy and decreasing computational cost. As a parameterized DA model, ANCHOR represents the ionosphere using physical parameters such as the F2 layer peak (F2), which characterizes a region's maximum electron density. These parameters are derived from various data sources and assimilated to generate two‐dimensional ionospheric state estimates. Originally, ANCHOR utilized data from ionosondes, radio occultation (RO) electron density profiles, and slant Total Electron Content (sTEC) from ground‐based Global Navigation Satellite System (GNSS) stations. This work extends the ground‐based approach by incorporating GNSS reflectometry (GNSS‐R) data; GNSS signals reflected off Earth's surface, captured by low Earth orbit satellites, to estimate sTEC in data‐sparse regions. GNSS‐R sTEC measurements along combined incident and reflected raypaths are used to extract ionospheric parameters, refining model F2 estimates to better match the true ionospheric state. F2 is extracted analytically along each ray path using a background model (PyIRI) and total sTEC. Modeled sTEC is used to estimate a weighting factor that apportions the total sTEC between the incident and reflected paths. An observational system simulation experiment (OSSE) demonstrates the reduction of global F2 root mean square error following assimilation. Results show a 36.8% linear reduction with a single day of GNSS‐R data (140 individual reflection events) with up to 47.3% reduction when a full week (1,018 events) is assimilated. These results highlight the utility of GNSS‐R data for enhancing ionospheric state estimation within a DA framework.