[A-AS05] Weather, Climate, and Environmental Science Studies using High-Performance Computing

[AAS05-07] Big Data Assimilation: Real-time 30-s-update Forecast Experiments Using Fugaku in Tokyo in 2021

*Takemasa Miyoshi¹, Arata Amemiya¹, Takumi Honda¹, Shigenori Otsuka¹, Yasumitsu Maejima¹, James Taylor¹, Hirofumi Tomita¹, Seiya Nishizawa¹, Kenta Sueki¹, Tsuyoshi Yamaura¹, Yutaka Ishikawa², Shinsuke Satoh³, Tomoo Ushio⁴, Kana Koike⁵, Erika Hoshi⁵ (1.RIKEN, 2.National Institute of Informatics, 3.National Institute of Information and Communications Technology, 4.Osaka University, 5.MTI Ltd.)

Keywords:Numerical Weather Prediction, Data Assimilation, Big Data, Local Torrential Rain, Phased Array Weather Radar

The Japan’s Big Data Assimilation (BDA) project started in October 2013 and ended its 5.5-year period in March 2019. Here, we developed a novel numerical weather prediction (NWP) system at 100-m resolution updated every 30 seconds for precise prediction of individual convective clouds. This system was designed to fully take advantage of the phased array weather radar (PAWR) which observes reflectivity and Doppler velocity at 30-second frequency for 100 elevation angles at 100-m range resolution. By the end of the 5.5-year project period, we achieved less than 30-second computational time using the Japan’s flagship K computer for past cases with all input data such as boundary conditions and observation data being ready to use. The direct follow-on project started in April 2019 for three years (i.e., ending soon in March 2022). We continued the development and achieved real-time operations of this novel 30-second-update NWP system for demonstration at 500-m resolution during July 31 and August 7, 2020, using the supercomputer Oakforest-PACS operated jointly by the Tsukuba University and the University of Tokyo. In 2021, we performed real-time experiments during two periods corresponding to the Tokyo Olympic and Paralympic games, i.e., July 20-August 8 and August 24-September 5, with an enhanced system using the new Japan’s flagship supercomputer Fugaku, ranked #1 in the most recent top500 list. Taking advantage of the computing power, we increased the ensemble size from 50 to 1000 for the local ensemble transform Kalman filter (LETKF). This presentation will summarize the real-time demonstration in 2021 and discuss future perspectives based on the 8.5-year-long project efforts.