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講演題目

Recent Progress on Big Data Assimilation in Numerical Weather Prediction

詳細
開催日 2019年8月5日(月)
開催時間 13:55 - 14:50
開催都市 兵庫県神戸市
場所

R-CCS 6階講堂

使用言語 発表・スライド共に英語
登壇者

三好 建正

データ同化研究チーム、チームリーダー

写真:三好 建正

講演要旨

The Big Data Assimilation (BDA) project in numerical weather prediction (NWP) started in October 2013 under the Japan Science and Technology Agency (JST) CREST program, and ended its 5.5-year period in March 2019. The direct follow-on project was accepted and started in April 2019 under the JST AIP Acceleration Research, with emphases on the connection with AI technologies, in particular, an integration of DA and AI with high-performance computation (HPC). The BDA project aimed to fully take advantage of “big data” from advanced sensors such as the phased array weather radar (PAWR) and Himawari-8 geostationary satellite, which provide two orders of magnitude more data than the previous sensors. We have achieved successful case studies with newly-developed 30-second-update, 100-m-mesh NWP system based on the RIKEN’s SCALE model and local ensemble transform Kalman filter (LETKF) to assimilate PAWR in Osaka and Kobe. We have been actively developing the workflow for real-time weather forecasting. In addition, we developed two precipitation nowcasting systems with the every-30-second PAWR data: one with an optical-flow-based system, the other with a deep-learning-based system. We chose the convolutional Long Short Term Memory (Conv-LSTM) as a deep learning algorithm, and found it effective for precipitation nowcasting. The use of Conv-LSTM would lead to an integration of DA and AI with HPC. This presentation will include an overview of the BDA project toward a DA-AI-HPC integration under the new AIP Acceleration Research scheme, and recent progress of the project.

(2019年8月1日)