GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients
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DOI：
10.3389/feart.2020.00182
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Syachrul Arief;K. Heki
中科院分区：
文献类型：
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作者：
Syachrul Arief;K. Heki
我们研究了2017-2019年夏季日本西南部的灾难性强降雨事件，特别是在九州地区，使用日本密集的全球导航卫星系统（GNSS）网络GEONET（GNSS地球观测网络）的对流层延迟数据。该地区在夏季经常遭受与静止锋相关的特大暴雨。在这项研究中，我们首先分析了2018年7月6日的水汽行为，使用从里诺的内华达州大学数据库获得的对流层参数。数据集包括对流层延迟梯度矢量（G），以及对流层天顶延迟（ZTD），每5分钟估计一次。首先，我们插值G以获得网格点处的延迟，并计算其收敛性，类似于Shoji（2013）提出的水汽浓度（WVC）指数。通过去除天顶流体静力延迟，得到了天顶湿延迟（ZWD）。原始ZWD值并不真正反映全球导航卫星系统台站上方大气的湿度，因为它们在很大程度上取决于台站的高度。为了研究暴雨前水汽的动态，我们估算了转换为海平面值的ZWD。在反演方案中，我们在所有GEONET站点使用G，并在低海拔（50 mm/h）事件发生时使用ZWD数据，即2017年7月5日，2018年7月6日和2019年8月27日。接下来，我们在大雨的日子里进行了高时间分辨率分析（每5分钟）。结果表明，暴雨前WVC和海平面ZWD均呈上升趋势，暴雨开始后ZWD迅速下降。然而，这两个量在多大程度上有助于预测暴雨，这是一个未来的问题。
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 (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.