GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients

We studied disastrous heavy rainfall episodes in 2017–2019 summer in SW Japan, especially in the Kyushu region using tropospheric delay data from the Japanese dense global navigation satellite system (GNSS) network GEONET (GNSS Earth Observation Network). This region often suffers from extremely hea...

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Main Authors: Syachrul Arief, Kosuke Heki
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Earth Science
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/feart.2020.00182/full
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spelling doaj-01b312a2418a40d683a5aef7168589ff2020-11-25T02:25:18ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632020-06-01810.3389/feart.2020.00182529669GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay GradientsSyachrul Arief0Syachrul Arief1Kosuke Heki2Department of Natural History Sciences, Hokkaido University, Sapporo, JapanGeospatial Information Agency, Cibinong, IndonesiaDepartment of Natural History Sciences, Hokkaido University, Sapporo, JapanWe studied disastrous heavy rainfall episodes in 2017–2019 summer in SW Japan, especially in the Kyushu region using tropospheric delay data from the Japanese dense global navigation satellite system (GNSS) network GEONET (GNSS Earth Observation Network). This region often suffers from extremely heavy rains associated with stationary fronts during summer. In this study, we first analyze behaviors of water vapor on July 6, 2018, using tropospheric parameters obtained from the database at the University of Nevada, Reno. The data set includes tropospheric delay gradient vectors (G), as well as zenith tropospheric delays (ZTD), estimated every 5 min. At first, we interpolated G to obtain those at grid points and calculated their convergence, similar to the quantity proposed by Shoji (2013) as water vapor concentration (WVC) index. We obtained zenith wet delay (ZWD) from ZTD by removing zenith hydrostatic delay. The raw ZWD values do not really reflect the wetness of the atmosphere above the GNSS station because they largely depend on the station altitudes. To study the dynamics of water vapor before heavy rains, we estimated ZWD converted to the values at sea level. In the inversion scheme, we used G at all GEONET stations and ZWD data at low-altitude (<100 m) GEONET stations as the input. Then we assumed that spatial change of ZWD is proportional to G (e.g., Gx = H ∂ZWD/∂x, where H is the water vapor scale height) and estimated sea-level ZWD at grid points all over Japan. At last, we tried to justify our working hypothesis that heavy rain occurs when both WVC and sea-level ZWD are high by analyzing hourly water vapor distributions in all the days in July 2017, July 2018, and August 2019. We found that both two values showed maxima in the studied period when the three heavy rainfall (>50 mm/h) episodes occurred, that is, July 5, 2017, July 6, 2018, and August 27, 2019. Next, we performed high time resolution analysis (every 5 min) on the days of heavy rain. The results suggest that both WVC and sea-level ZWD go up prior to the onset of the rain, and ZWD decreases rapidly once the heavy rain started. It is a future issue, however, how far these two quantities contribute to forecast heavy rains.https://www.frontiersin.org/article/10.3389/feart.2020.00182/fullheavy rainGNSSJapantropospheric gradientwater vapor convergencezenith wet delay
collection DOAJ
language English
format Article
sources DOAJ
author Syachrul Arief
Syachrul Arief
Kosuke Heki
spellingShingle Syachrul Arief
Syachrul Arief
Kosuke Heki
GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
Frontiers in Earth Science
heavy rain
GNSS
Japan
tropospheric gradient
water vapor convergence
zenith wet delay
author_facet Syachrul Arief
Syachrul Arief
Kosuke Heki
author_sort Syachrul Arief
title GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
title_short GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
title_full GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
title_fullStr GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
title_full_unstemmed GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
title_sort gnss meteorology for disastrous rainfalls in 2017–2019 summer in sw japan: a new approach utilizing atmospheric delay gradients
publisher Frontiers Media S.A.
series Frontiers in Earth Science
issn 2296-6463
publishDate 2020-06-01
description We studied disastrous heavy rainfall episodes in 2017–2019 summer in SW Japan, especially in the Kyushu region using tropospheric delay data from the Japanese dense global navigation satellite system (GNSS) network GEONET (GNSS Earth Observation Network). This region often suffers from extremely heavy rains associated with stationary fronts during summer. In this study, we first analyze behaviors of water vapor on July 6, 2018, using tropospheric parameters obtained from the database at the University of Nevada, Reno. The data set includes tropospheric delay gradient vectors (G), as well as zenith tropospheric delays (ZTD), estimated every 5 min. At first, we interpolated G to obtain those at grid points and calculated their convergence, similar to the quantity proposed by Shoji (2013) as water vapor concentration (WVC) index. We obtained zenith wet delay (ZWD) from ZTD by removing zenith hydrostatic delay. The raw ZWD values do not really reflect the wetness of the atmosphere above the GNSS station because they largely depend on the station altitudes. To study the dynamics of water vapor before heavy rains, we estimated ZWD converted to the values at sea level. In the inversion scheme, we used G at all GEONET stations and ZWD data at low-altitude (<100 m) GEONET stations as the input. Then we assumed that spatial change of ZWD is proportional to G (e.g., Gx = H ∂ZWD/∂x, where H is the water vapor scale height) and estimated sea-level ZWD at grid points all over Japan. At last, we tried to justify our working hypothesis that heavy rain occurs when both WVC and sea-level ZWD are high by analyzing hourly water vapor distributions in all the days in July 2017, July 2018, and August 2019. We found that both two values showed maxima in the studied period when the three heavy rainfall (>50 mm/h) episodes occurred, that is, July 5, 2017, July 6, 2018, and August 27, 2019. Next, we performed high time resolution analysis (every 5 min) on the days of heavy rain. The results suggest that both WVC and sea-level ZWD go up prior to the onset of the rain, and ZWD decreases rapidly once the heavy rain started. It is a future issue, however, how far these two quantities contribute to forecast heavy rains.
topic heavy rain
GNSS
Japan
tropospheric gradient
water vapor convergence
zenith wet delay
url https://www.frontiersin.org/article/10.3389/feart.2020.00182/full
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