Capturing Extreme Water Vapor and Instability With High‐Resolution <scp>GNSS</scp> Monitoring

ABSTRACT This study utilized data from a high‐resolution Global Navigation Satellite System (GNSS) network with a horizontal spacing of less than 10 km, including stations operated by private companies, to analyze the horizontal and vertical distributions of water vapor during a sustained heavy precipitation event in Japan. The network revealed concentrated regions of high precipitable water vapor (PWV &gt; 70 mm) within a narrow area, accompanied by the development of a moist absolutely unstable layer (MAUL). PWV values approached 80 mm in some areas, and a tomographic analysis identified a deep moist layer associated with heavy rainfall and abundant moisture. These findings demonstrate that the high‐resolution GNSS network effectively captured the fine‐scale three‐dimensional water vapor structure associated with a heavy rainfall system composed of multiple deep convective clouds. The results suggest its potential to enhance water vapor monitoring and forecasting for extreme precipitation events.

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Publication Details

Journal
Atmospheric Science Letters
Published
2026-02-01
DOI
https://doi.org/10.1002/asl2.70010
Primary Topic
GNSS positioning and interference
Type
article
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article

Capturing Extreme Water Vapor and Instability With High‐Resolution GNSS Monitoring

Mikiko Fujita
Atmospheric Science Letters
GNSS positioning and interference
article

Capturing Extreme Water Vapor and Instability With High‐Resolution GNSS Monitoring

Mikiko Fujita
article en

Abstract

ABSTRACT This study utilized data from a high‐resolution Global Navigation Satellite System (GNSS) network with a horizontal spacing of less than 10 km, including stations operated by private companies, to analyze the horizontal and vertical distributions of water vapor during a sustained heavy precipitation event in Japan. The network revealed concentrated regions of high precipitable water vapor (PWV &gt; 70 mm) within a narrow area, accompanied by the development of a moist absolutely unstable layer (MAUL). PWV values approached 80 mm in some areas, and a tomographic analysis identified a deep moist layer associated with heavy rainfall and abundant moisture. These findings demonstrate that the high‐resolution GNSS network effectively captured the fine‐scale three‐dimensional water vapor structure associated with a heavy rainfall system composed of multiple deep convective clouds. The results suggest its potential to enhance water vapor monitoring and forecasting for extreme precipitation events.

Atmospheric Science LettersVol. 27(2)
Japan Agency for Marine-Earth Science and Technology (JP)
Clean water and sanitation
Openalex Percentile: Top 61%
GNSS positioning and interference
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