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Yasumitsu Maejima

dblp:187/9753 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0006-2121-1044ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 100%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Environmental and earth informatics · 72% Computational science and engineering · 28%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › large-scale simulation
numerical weather prediction
0.922023
Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023
"Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress · Proc. IEEE 2016
High-performance computing
performance optimization at scale
0.712023
Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023
High-performance computing › scientific computing
data assimilation
0.212016
"Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress · Proc. IEEE 2016
Environmental and earth informatics
atmospheric science
0.212023
Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023

Methods — techniques the papers use, named apart from their topics

single precision · 1.3parallel i/o · 1.3ensemble methods · 1.3
YearPublicationVenuePosition
2025 Dimensionality Reduction-based Interactive Visual Analytics Approach for Investigating Ensemble Weather Simulations
Go Tamura, Sena Kobayashi, Naohisa Sakamoto, Yasumitsu Maejima, Jorji Nonaka
HPC Asia4
2023 Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics
abstract
Real-time 30-second-refresh numerical weather prediction (NWP) was performed with exclusive use of 11,580 nodes (~7%) of supercomputer Fugaku during Tokyo Olympics and Paralympics in 2021. Total 75,248 forecasts were disseminated in the 1-month period mostly stably with time-to-solution less than 3 minutes for 30-minute forecast. Japan's Big Data Assimilation (BDA) project developed the novel NWP system for precise prediction of hazardous rains toward solving the global climate crisis. Compared with typical 1-hour-refresh systems, the BDA system offered two orders of magnitude increase in problem size and revealed the effectiveness of 30-second refresh for highly nonlinear, rapidly evolving convective rains. To achieve the required time-to-solution for real-time 30-second refresh with high accuracy, the core BDA software incorporated single precision and enhanced parallel I/O with properly selected configurations of 1000 ensemble members and 500-m-mesh weather model. The massively parallel, I/O intensive real-time BDA computation demonstrated a promising future direction.
Takemasa Miyoshi, Arata Amemiya, Shigenori Otsuka, Yasumitsu Maejima, Takumi Honda, Hirofumi Tomita, Seiya Nishizawa, Kenta Sueki, Tsuyoshi Yamaura, Yutaka Ishikawa, Shinsuke Satoh, Tomoo Ushio, Kana Koike, Atsuya Uno
SC4
2016 "Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress
abstract
Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.
Takemasa Miyoshi, Guo-Yuan Lien, Shinsuke Satoh, Tomoo Ushio, Kotaro Bessho, Hirofumi Tomita, Seiya Nishizawa, Ryuji Yoshida, Sachiho A. Adachi, Jianwei Liao 0001, Balazs Gerofi, Yutaka Ishikawa, Masaru Kunii, Yasumitsu Maejima, Shigenori Otsuka, Michiko Otsuka, Kozo Okamoto, Hiromu Seko
Proc. IEEE15