As a critical trace gas and a sensitive indicator of climate change, water vapor (H₂O) plays a pivotal role in regulating the Earth’s radiative budget and middle-atmospheric chemical cycles. In this study, H₂O measurements from the Sounding of the Atmosphere using a Broadband Emission Radiometry (SABER) instrument on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite are analyzed to characterize the H₂O spatiotemporal distribution throughout the stratosphere–mesosphere–lower thermosphere (SMLT) region. Using eigen analysis, the bimonthly mean H₂O across different latitude bins between 2002 and 2025 is decomposed into four empirical orthogonal functions (EOFs). Results indicate that stratospheric water vapor remains relatively stable with weak latitudinal dependence, whereas H₂O in the mesosphere–lower thermosphere (MLT) at middle-to-high latitudes exhibits pronounced seasonal variations and distinct hemispheric antisymmetry. The first mode captures a global H₂O long-term increasing trend. Both EOF1 and EOF2 are associated with solar activity and the El Niño–Southern Oscillation (ENSO) to varying degrees, indicating that these dominant modes are driven by multiple concurrent forcing factors. EOF3 correlates with the Quasi-Biennial Oscillation (QBO), suggesting links to QBO-driven atmospheric dynamical processes, whereas EOF4 demonstrates no significant associations with natural activity indices, suggesting perturbations arising from alternative atmospheric mechanisms. By systematically applying EOF analysis to a 24-year dataset of H₂O observations in the SMLT region, this study characterizes the principal distribution patterns and evolutionary characteristics of SMLT H₂O. Through correlation analyses with natural forcing indices, the complex driving mechanisms governing its variability are elucidated. This work provides a comprehensive observational framework for SMLT water vapor variability, enhancing assessments of SMLT H₂O responses to long-term climate change and refining the understanding of the Earth–atmosphere system. Furthermore, these findings provide critical data support for the subsequent development and optimization of SMLT water vapor models.

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 demonstrate a substantial improvement in the precision of the thermospheric wind field. This enhancement is reflected in a 45–50% reduction in Root Mean Square Error (RMSE) for both zonal and meridional components. 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 refining the spatial structures and reshaping cross-equatorial flows, the forecast skill for this component dissipated rapidly. This characteristic of “short memory” underscores the highly dynamic nature of thermospheric winds and emphasizes the need for high-frequency assimilation cycles. The system required a spin-up period of approximately 8 h to achieve statistical stability. These findings demonstrate that the assimilation of data from ICON/MIGHTI satellites not only diminishes numerical inaccuracies but also improves the representation of instantaneous thermospheric wind distributions. Providing a high-fidelity dataset is crucial for advancing the modeling and understanding of the complex interactions within the Earth’s ionosphere-thermosphere system.