Japan Geoscience Union Meeting 2019

Presentation information

[J] Oral

M (Multidisciplinary and Interdisciplinary) » M-AG Applied Geosciences

[M-AG39] Marine-Earth Informatics

Thu. May 30, 2019 1:45 PM - 3:15 PM A10 (TOKYO BAY MAKUHARI HALL)

convener:Seiji Tsuboi(JAMSTEC, Center for Earth Information Science and Technology), Keiko Takahashi(Japan Agency for Marine and Earth Science and Technology), Masaki Kanao(National Institute of Polar Research), Daisuke Matsuoka(Japan Agency for Marine-Earth Science and Technology), Chairperson:Daisuke Matsuoka, Seiji Tsuboi

2:15 PM - 2:30 PM

[MAG39-03] Automatic detection of weather front around Japan using deep convolutional neural network

*Daisuke Matsuoka1,2, Shiori Sugimoto1, Yujin Nakagawa1, Fumiaki Araki1, Shintaro Kawahara1, Yosuke Onoue4, Masaaki Iiyama3, Koji Koyamada3 (1.Japan Agency for Marine-Earth Science and Technology, 2.Japan Science and Technology Agency, 3.Nihon University, 4.Kyoto University)

Keywords:Deep learning, Convolutional neural network, Weather front detection

In this study, we automatically detect stationary front from weather forecast simulation data (GPV/MSM) using U-Net deep convolutional neural network. Our U-Net trains ten years weather data (precipitation, sea level pressure, relative humidity, air temperature, and wind velocity) and the position of weather front as the ground truth. As a result of applying the trained U-Net to the untrained one year data, our approach succeeded in accurately detecting Baiu front and autumn rain front except when Typhoon occurred. Moreover, wind velocity (zonal and meridional component) and relative humidity at 1000 hPa play an important role to obtain high detection performance. Our approach is also able to apply to weather simulation data which the weather front is not associated with.