Assimilation of ICON/MIGHTI wind profiles into a coupled thermosphere/ionosphere model using Ensemble Square Root filter

Precise characterization of the thermospheric neutral wind is essential for comprehending the dynamic interactions within the ionosphere-thermosphere system, as evidenced by the development of models like HWM and the need for localized data. However, numerical models often suffer from biases due to uncertainties in external forcing and the scarcity of direct wind observations. This study examines the influence of incorporating actual neutral wind profiles from the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on the Ionospheric Connection Explorer (ICON) satellite into the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) via an ensemble-based data assimilation framework. To address the challenges of assimilating real observational data, a robust background check Quality Control (QC) scheme with dynamic thresholds based on ensemble spread was implemented. The assimilation performance was evaluated by comparing the analysis results against independent, unassimilated observations and a free-running model Control Run. The findings suggest a significant enhancement in the precision of the thermospheric wind field, as evidenced by a substantial decrease in the Root Mean Square Error (RMSE) for both zonal and meridional components, with reductions ranging from 45% to 50%. For zonal winds, the system demonstrated effective bias removal and sustained forecast skill, indicating a strong model memory of the large-scale mean flow. In contrast, while the assimilation exceptionally corrected the meridional circulation by adjusting tidal propagation phases and reshaping cross-equatorial flows, the forecast skill for this component dissipated rapidly. This characteristic of "short memory" underscores the highly dynamic nature of tide-driven fluctuations and emphasizes the need for high-frequency assimilation cycles. The system required a spin-up period of approximately 8 hours to achieve statistical stability. These findings demonstrate that The assimilation of data from ICON/MIGHTI satellites not only diminishes numerical inaccuracies but also enhances the physical delineation of thermospheric tidal patterns. Providing a high-fidelity dataset is crucial for advancing the modeling and understanding of the complex interactions within the Earth's ionosphere-thermosphere system.