HDTM: A Novel Model Providing Hydrostatic Delay and Weighted Mean Temperature for Real-Time GNSS Precipitable Water Vapor Retrieval

Timely zenith hydrostatic delay (ZHD) and weighted mean temperature ( T-m ) are critical for real-time GNSS precipitable water vapor (PWV) retrieval. However, for GNSS stations without collocated meteorological sensors, ZHD and T-m data are often inaccessible. Although a T-m ospheric reanalysis offers an accurate alternative, its latency impedes real-time GNSS PWV retrieval. In this study, we propose a novel model, HD T-m , capable of providing hourly-updated ZHD and T-m forecast grids using freely available numerical weather predictions, NCEP-GFS and ECMWF-IFS. The HD T-m model was validated over land and ocean regions in China, utilizing data from ERA5 reanalysis, 953 GNSS/meteorological stations, 64 radiosonde stations, and oceanic in-situ pressure measurements. The results demonstrate that: (1) the HD T-m model outperforms the traditional models, particularly in capturing the diurnal variations of ZHD and T-m , with ZHD root mean square (RMS) errors of 3.2 mm (1.9 mm over oceans) and T-m RMS error of 1.5 K. (2) PWV values retrieved using HD T-m exhibit negligible discrepancies from those retrieved using in-situ meteorological parameters, with a mean RMS of 0.9 mm across China. (3) In two extreme rainfall events, HD T-m demonstrated superior accuracy in capturing ZHD and T-m and retrieved highly variable PWV with an RMS of 1.1 mm. Overall, HD T-m can provide high-quality ZHD and T-m forecasts under both stable and turbulent weather conditions, facilitating precise real-time GNSS PWV monitoring over both land and ocean without relying on collocated meteorological sensors.