HDTM: A novel model providing hydrostatic delay and weighted mean temperature for real-time GNSS precipitable water vapor retrieval

• Published in: IEEE transactions on geoscience and remote sensing 2024-12, p.1-1
• Main Authors: Li, Luohong, Zhang, Hongxing, Yuan, Yunbin, Aichinger-Rosenberger, Matthias, Soja, Benedikt
• Format: Article
• Language: English
• Subjects: Accuracy; Atmospheric modeling; Data models; Global navigation satellite system; Meteorology; Ocean temperature; Precipitable water vapor (PWV); Predictive models; Real-time GNSS; Real-time systems; Severe rainfall; Temperature sensors; Weather forecasting; Weighted mean temperature (<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">T m ); Zenith hydrostatic delay (ZHD)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3519428

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.