Japan Geoscience Union Meeting 2018

Presentation information

[EE] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI22] Data assimilation: A fundamental approach in geosciences

Sun. May 20, 2018 9:00 AM - 10:30 AM 302 (3F International Conference Hall, Makuhari Messe)

convener:Shin'ya Nakano(The Institute of Statistical Mathematics), Yosuke Fujii(Meteorological Research Institute, Japan Meteorological Agency), SHINICHI MIYAZAKI(京都大学理学研究科, 共同), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Chairperson:Fujii Yosuke

9:15 AM - 9:30 AM

[MGI22-02] Grad-CAM will tell the important regions to predict the typhoon intensity

*Shinya Tanahara1, Kosuke Ito1,2, Hiroyuki Yamada1, Taiga Shibata1, Ryota Miyata1 (1.University of the Ryukyus, 2.Meteorological Research Institute)

Keywords:Typhoon intensity prediction, Artificial intelligence, Convolutional neural network, Gradient-weighted class activation mapping, Sensitivity analysis

Because typhoons are often highly destructive, their accurate prediction has been of particular importance in the field of weather forecasting. Although there has been relatively steady improvement over the years in track forecasting with ever improving numerical models, the accuracy of intensity forecasts still lags that of the track forecasts. We here adopt an artificial intelligence approach essentially different from the conventional one based on the global spectral model. we predict the 24-hour typhoon intensities from the past satellite images using the convolutional neural network (CNN), which has been established as a powerful classification model for image recognition problems. Moreover, we conduct a sensitivity analysis of the prediction model using a gradient-weighted class activation mapping (Grad-CAM) technique, which produces a localization map highlighting the important regions in the image for predicting 24 hours after typhoon intensity. The results suggest that the shape of clouds surrounding the core of typhoon such as rainbands is more crucial than that of the typhoon itself to predict the intensity.