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 3:30 PM - 5:00 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:Seiji Tsuboi, Daisuke Matsuoka

4:20 PM - 4:35 PM

[MAG39-10] Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology

*Ryo Onishi1, DAISUKE SUGIYAMA1, Keigo Matsuda1 (1.Japan Agency for Marine-Earth Science and Technology)

Keywords:super-resolution, deep learning, building-resolving urban micrometeorology, multi-scale data assimilation, IoT

We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN). The CNN is trained using pairs of high-resolution (HR) and low-resolution (LR) images created from meteorological simulation results for different resolutions so that it can map LR simulation images to HR ones. The proposed SR simulation system, which performs LR simulations, can provide HR prediction results in much shorter operating cycles than those required for corresponding HR simulation prediction system. We apply the SR simulation system to urban micrometeorology, which is strongly affected by buildings and human activity. Urban micrometeorology simulations that need to resolve urban buildings are computationally costly and thus cannot be used for operational real-time predictions even when run on supercomputers. We performed HR micrometeorology simulations on a supercomputer to obtain datasets for training the CNN in the SR method. It is shown that the proposed SR method can be used with a spatial scaling factor of 4 and that it outperforms conventional interpolation methods by a large margin. It is also shown that the proposed SR simulation system has the potential to be used for operational urban micrometeorology predictions.