GNSS Precipitable Water Vapor Assimilation for Improving Numerical Weather Prediction of Meteorological Parameters in Small Watersheds

Accurate short-term forecasts of meteorological parameters remain challenging in mountainous small watersheds because sparse observations and global background fields do not adequately capture rapid local water vapor variations. Previous studies indicate that assimilating GNSS-derived PWV can improve regional-scale forecasts of precipitation and near-surface meteorological variables. However, its incremental value in mountainous small watersheds remains uncertain. Complex terrain, sparse local observations, and rapid water vapor variability may limit the transferability of findings obtained over larger domains. To address this setting-specific gap, we assess the impact of GNSS-derived PWV assimilation on short-term numerical forecasts for the Shahe watershed and its surrounding area in Hanzhong, China. Using the Weather Research and Forecasting Data Assimilation (WRFDA) system, the MET experiment assimilated conventional meteorological observations from four GNSS meteorological (GNSS-Met) stations, whereas MET + PWV additionally assimilated GNSS-derived PWV from the same four stations. The 129 CMA meteorological stations were reserved exclusively for independent forecast verification. During the selected precipitation period, adding GNSS-derived PWV reduces hourly precipitation RMSE by 4.7% compared with assimilating conventional meteorological observations alone. It also increases the threat score (TS) and probability of detection (POD) by 3.8% and 5.0%, respectively. Moreover, adding GNSS-derived PWV reduces the mean RMSEs of air temperature and relative humidity by 10.2% and 8.0%, respectively, while the corresponding reduction in surface pressure RMSE is 2.9%. During the selected non-precipitation period, adding GNSS-derived PWV to conventional meteorological-observation assimilation reduces the 1–6 h RMSEs of air temperature, relative humidity, and surface pressure by 6.0–8.9%, 8.3–9.8%, and 4.2–4.3%, respectively. For the selected precipitation and non-precipitation cases, the results indicate that adding GNSS-derived PWV to conventional meteorological observations from the same four GNSS-Met stations provides an additional local moisture constraint and improves short-term forecasts of multiple meteorological parameters in the Shahe watershed and its surrounding area.