Modeling of Middle Atmospheric Water Vapor Based on TIMED/SABER Data

Water vapor (H2O) acts as both an essential thermodynamic driver and a primary source of chemical radicals in the middle atmosphere, playing an irreplaceable role in maintaining Earth’s radiative balance and indicating long-term climate variability. In this study, 24 years (2002–2025) of H2O measurements from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite are systematically analyzed to characterize the H2O spatiotemporal distribution throughout the middle atmosphere (specifically within the 20–80 km altitude range), with a focus on elucidating its evolutionary patterns across time, altitude, and latitude. Building upon this analysis, an empirical model for the bimonthly mean water vapor volume mixing ratio (VMR) is constructed based on actual measurements. Employing a nonlinear least-squares fitting algorithm, time-series fitting is performed on the data within distinct altitude and latitude grids. Consequently, a mathematical analytical expression for the time series was derived for each latitudinal band at every altitude grid point, alongside the determination of corresponding fitting parameter sets. By integrating these parameterized formulas and derived parameters, a comprehensive empirical H2O VMR model spanning multiple altitude layers and a broad latitudinal range was ultimately established. Validation results demonstrate that the empirical model exhibits high consistency with the original observational data. The coefficients of determination (R2) generally exceed 0.7 and strictly remain ≥ 0.6 in all cases. Furthermore, the model demonstrates strong linear correlation with actual observations (Pearson correlation coefficients typically exceeding 0.8) and maintains low bias, as evidenced by small root mean square errors (mostly < 0.35 ppmv) and mean absolute errors (mostly < 0.25 ppmv) across diverse spatial grids. These evaluation metrics collectively indicate excellent goodness-of-fit and robust reconstruction capabilities. This model provides a reliable empirical reference for investigating the spatiotemporal evolution of middle atmospheric H2O VMR and serves as a potential data foundation for future optimizations of relevant radiative transfer models.