[M-IS09] Effects of lightning, severe weather and tropical storms

[MIS09-05] Proposal of a new method for predicting heavy rainfall using an electrostatic field observation system and discussion of its practical feasibility

*由井 祥¹、高橋 幸弘¹、佐藤 光輝¹、久保田 尚之¹、菅野 将史 (1.北海道大学)

Every year, torrential rains brought by cumulonimbus clouds cause a lot of damage, especially in Southeast Asia. Therefore, improving the accuracy of heavy rainfall prediction has become an urgent social issue. in 1982, Piepgrass et al. found that the frequency of lightning discharges changes about 5 minutes before heavy rain (Piepgrass et al., 1982). This suggests that detailed observations of lightning discharges can be used to make short time predictions of heavy rainfall. In this study, the source of lightning discharges can be determined in three dimensions by observing the changes in the electric field caused by lightning discharges and fitting them using the least-squares method. This makes it possible to estimate the interior of a cumulonimbus cloud from the ground. Although field mills are generally used to observe electric field changes in cumulonimbus clouds, this research uses a plate-type capacitance antenna, which is inexpensive and suitable for high-density observation at multiple locations. In a previous study, Kanno established a method for estimating the power source and calibrating the relative sensitivity in clouds using a plate-type capacitance antenna, and analyzed the results on 2020/07/11 07:06-07:15 UT (Kanno, 2021). In this study, we reduced the time required for the analysis algorithm, which is an issue in the practical application of this method. In this study, we centralized multiple analysis algorithms so that we can automatically perform everything from data acquisition to parameter derivation. For the calibration method of the relative sensitivity, which varies at each observation point, we shortened the calibration time by using a new method. In this study, I discuss the practicality of the above methods for heavy rainfall forecasting.This research was supported by Science and Technology Research Partnership for
Sustainable Development (SATREPS), Japan Science and Technology Agency
(JST) / Japan International Cooperation Agency (JICA).