Spatially enhanced interpolating vertical adjustment model for precipitable water vapor

As a critical parameter in meteorological monitoring, precipitable water vapor (PWV) is widely used in short-term extreme weather forecasting and long-term climate change research. However, as PWV exhibits significant vertical attenuation, especially within 2 km, achieving accurate vertical interpolation is essential for comparisons and fusion across different measurement techniques, such as sampling water vapor at different heights. PWV vertical adjustment relies only on the empirical or time-varying lapse rate models (e.g., GPWV-H). The non-uniform vertical distribution of PWV and the uncertain variation trend in the low-latitude region still limit the accuracy. To address these issues, we propose the Spatially enhanced Vertical Adjustment Model for PWV (SPWV-H), taking into account the non-uniform distribution in the vertical direction based on the fifth-generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5) products. The assessment, validated against the ERA5 benchmark, highlights the SPWV-H model’s superior performance with an RMSE of 1 mm and a bias of 0.03 mm, especially pronounced in the low-latitude region. Compared to global radiosonde datasets, the SPWV-H model achieves notable reductions in RMSE of 12%, 11%, and 18% when evaluated against the EPWV-H, GPWV-H, and GPT3-1 models, respectively. In spatial interpolation, the SPWV-H model achieves an RMSE of 1.22 mm, indicating an improvement of 10%, 9%, and 14% compared to the EPWV-H, GPWV-H, and GPT3-1 models, respectively. Therefore, the SPWV-H model can provide a reliable service for multi-source PWV fusion and real-time PWV monitoring by GNSS.