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September 20, 2025Remote Sensing5 citationsOpen Access

Analysis of GNSS Precipitable Water Vapor and Its Gradients During a Rainstorm in North China in July 2023

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HSHualin SuYWYizhu WangYCYunchang Cao

Key Points

  • GNSS-derived data reveals strong connections between precipitation and water vapor gradients during rainstorms.
  • Peak precipitable water vapor levels over 60 mm signal the likelihood of significant rainfall events.
  • Methodology includes spatiotemporal comparisons utilizing radar and precipitation data from multiple GNSS sites.
  • Findings suggest GNSS water vapor data can enhance short-range forecasting for extreme rainstorm occurrences.

Abstract

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events.

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Cite This Study

Su et al. (2025) studied this question.

synapsesocial.com/papers/68d46fc631b076d99fa69b43https://doi.org/10.3390/rs17183247
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