9:00 AM - 10:30 AM
[ACG37-P06] Analysis result of the downburst which occurred when GPM/DPR was passing over the high density ground surface meteorological observation network (Aug 27, 2018 in Saitama prefecture)
Keywords:GPM/DPR, POTEKA, rainfall intensity, precipitation, gust prediction, downburst
Meisei Electric has composed the high density ground surface meteorological observation network of about 30 km north-south and about 60km east-west with approximately up to 150 POTEKAs in the northern Kanto plains of Gunma, Saitama and Tochigi prefectures and has continued the constantly fixed-point meteorological observation since 2013. In parallel, we have developed the technology which predict the gust caused by the extremely localized weather changes such as tornadoes and downbursts with the real time observation data by a POTEKA network. This technology feature is that the less eminent the prediction objective of an extreme weather change is, the less the prediction accuracy is. However, in simulation, this technology could predict the gust damaged area precisely for the three eminent downbursts of the F1/JEF1 category of Aug 11, 2013, Jun 15, 2015 and Jul 14, 2016 in the northern Kanto plains. Moreover, it was confirmed that beforehand informing could be executed approximately 20 minutes before the gust damage occurrence to the residents in the damaged area.
However, it is of course desirable that the lead time which is from beforehand informing to the gust damage occurrence is as long as possible. Moreover, the customers who use POTEKA require that the lead time is approximately 30 minutes as the ideal. The prediction accuracy and the lead time are strong dependent on the degree of the high density and the wideness of a POTEKA network. However, Japan is the islands where the residential areas are very small because the most of the lands are mountains. Therefore, there are many cases in which we cannot realize more a dense and wide POTEKA network because of the geographical restrictions and the customer’s needs. We sometimes feel the limit of the ground surface meteorological observation.
On the other hand, although the satellite meteorological observation cannot execute with a high density and fixed-point way like as the ground surface, it can pass over the mountains and the seas without the geographical restrictions and can observe very widely. In addition, the extremely localized weather changes such as tornadoes and downbursts are thought to be caused by the strong cumulonimbus in the sky. Since the satellite observation can observe the precipitation (rainfall intensity) in the sky, it may be able to detect the sign of these weather changes before the occurrence.
It is very rare that GPM satellite passes over the ground surface just before or while the occurrence of the extremely localized weather changes such as tornadoes and downbursts. However, on Aug 27, 2018, when the atmosphere was unstable over the wide area of Kanto region and the damages by the downbursts were reported in Gunma, Saitama and Tokyo prefectures from around 17:00 JST to 19:00 JST, GPM satellite was passing over around 18:20 JST. In addition, Japan Meteorological Agency reported that the gust damage occurred in Warabi City, Saitama Prefecture around 18:30 and we have installed approximately 10 POTEKAs in Kawaguchi City, east of Warabi City. The relationship between GPM/DPR rainfall intensity data and POTEKA precipitation data may reveal to the significant time relation between the sky and the ground surface just before or while the downburst occurrence. Moreover, the analysis for the time relation may lead to obtain the knowledge in order to secure the more lead time in POTEKA gust prediction technology.
I will report the analysis result in this presentation.
However, it is of course desirable that the lead time which is from beforehand informing to the gust damage occurrence is as long as possible. Moreover, the customers who use POTEKA require that the lead time is approximately 30 minutes as the ideal. The prediction accuracy and the lead time are strong dependent on the degree of the high density and the wideness of a POTEKA network. However, Japan is the islands where the residential areas are very small because the most of the lands are mountains. Therefore, there are many cases in which we cannot realize more a dense and wide POTEKA network because of the geographical restrictions and the customer’s needs. We sometimes feel the limit of the ground surface meteorological observation.
On the other hand, although the satellite meteorological observation cannot execute with a high density and fixed-point way like as the ground surface, it can pass over the mountains and the seas without the geographical restrictions and can observe very widely. In addition, the extremely localized weather changes such as tornadoes and downbursts are thought to be caused by the strong cumulonimbus in the sky. Since the satellite observation can observe the precipitation (rainfall intensity) in the sky, it may be able to detect the sign of these weather changes before the occurrence.
It is very rare that GPM satellite passes over the ground surface just before or while the occurrence of the extremely localized weather changes such as tornadoes and downbursts. However, on Aug 27, 2018, when the atmosphere was unstable over the wide area of Kanto region and the damages by the downbursts were reported in Gunma, Saitama and Tokyo prefectures from around 17:00 JST to 19:00 JST, GPM satellite was passing over around 18:20 JST. In addition, Japan Meteorological Agency reported that the gust damage occurred in Warabi City, Saitama Prefecture around 18:30 and we have installed approximately 10 POTEKAs in Kawaguchi City, east of Warabi City. The relationship between GPM/DPR rainfall intensity data and POTEKA precipitation data may reveal to the significant time relation between the sky and the ground surface just before or while the downburst occurrence. Moreover, the analysis for the time relation may lead to obtain the knowledge in order to secure the more lead time in POTEKA gust prediction technology.
I will report the analysis result in this presentation.