RIKEN Center for Computational Science Data Assimilation Research Team

Team Principal: Takemasa Miyoshi (Ph.D.)

Japanese Page

Research Summary

Data assimilation (DA) is a cross-disciplinary science to synergize computer simulations and real-world data, using statistical methods and applied mathematics. As computers become more powerful and enable more precise simulations, it will become more important to compare the simulations with actual observations. DA Team performs cutting-edge research and development on advanced DA methods and their wide applications, aiming to integrate computer simulations and real-world data in the wisest way. Particularly, DA Team tackles challenging problems of developing efficient and accurate DA systems for “big simulations” with real-world “big data” from various sources including advanced sensors. The specific foci include 1) theoretical and algorithmic developments for efficient and accurate DA, 2) DA methods and applications by taking advantage of the world-leading supercomputer and “big data” from new advanced sensors, and 3) exploratory new applications of DA in wider simulation fields. These advanced DA studies will enhance simulation capabilities and lead to a better use of high-performance computers.

Main Research Fields

Mathematical & Physical Sciences

Related Research Fields

Informatics

Keywords

Data Assimilation
Numerical Weather Prediction
Large Scale Simulation
Ensemble Data Assimilation
Natural disaster prediction

Research Subjects

Ensemble-based data assimilation suitable to various high-dimensional simulations
Theoretical research on challenging problems in data assimilation
Wide applications of data assimilation
Cutting-edge data assimilation research on geophysical applications
Data assimilation using Big Data

Selected Publications

1. Liang, J., N. Sugimoto, and T. Miyoshi,
"2025: Unveiling Energy Conversions of the Venus Atmosphere by the Bred Vectors. Geophysical Research Letters, 52, e2024GL112663."
doi:10.1029/2024GL1126631
2. Ohishi, S., T. Miyoshi, T. Ando, T. Higashiuwatoko, E. Yoshizawa, H. Murakami, and M. Kachi,
"2024: LETKF-based Ocean Research Analysis (LORA) version 1.0, Geoscience Data Journal, 11, 995-1006."
doi:10.1002/gdj3.2711
3. Li, L., J. Li, and T. Miyoshi,
"2024: Chaos suppression through Chaos enhancement, Nonlinear Dyn.",
doi:10.1007/s11071-024-10426-z1
4. Sun, Q., T. Miyoshi, S. Richard,
"2023: Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter., Journal of Computational and Applied Mathematics."
doi.org/10.1016/j.cam.2022.1147721
5. Miyoshi, T., A. Amemiya, S. Otsuka, Y. Maejima, J. Taylor, T. Honda, H. Tomita, S. Nishizawa, K. Sueki, T. Yamaura, Y. Ishikawa, S. Satoh, T. Ushio, K. Koike, and A. Uno,
"2023: Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, 8, 1-10."
doi:10.1145/3581784.36270471
6. Miyoshi, T. and Sun, Q.,
"2022: Control Simulation Experiment with the Lorenz's Butterfly Attractor, Nonlin. Processes Geophys., 29, 133-139, 2022."
doi.org/10.5194/npg-29-133-20221
7. Honda, T., S. Kotsuki, G.-Y. Lien, Y. Maejima, K. Okamoto and T. Miyoshi,
"2018: Assimilation of Himawari-8 All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction. J. Geophys. Res., 123, 965-976."
doi:10.1002/2017JD0270961
8. Miyoshi, T., G.-Y. Lien, S. Satoh, T. Ushio, K. Bessho, H. Tomita, S. Nishizawa, R. Yoshida, S. A. Adachi, J. Liao, B. Gerofi, Y. Ishikawa, M. Kunii, J. Ruiz, Y. Maejima, S. Otsuka, M. Otsuka, K. Okamoto, and H. Seko,
"2016: "Big Data Assimilation" toward Post-peta-scale Severe Weather Prediction: An Overview and Progress. Proc. of the IEEE, 104, 2155-2179."
doi:10.1109/JPROC.2016.26025601
9. Miyoshi, T., M. Kunii, J. Ruiz, G.-Y. Lien, S. Satoh, T. Ushio, K. Bessho, H. Seko, H. Tomita, and Y. Ishikawa,
"2016: "Big Data Assimilation" Revolutionizing Severe Weather Prediction. Bull. Amer. Meteor." Soc., 97, 1347-1354."
doi:10.1175/BAMS-D-15-00144.1
10. Miyoshi, T., K. Kondo, and T. Imamura,
"2014: The 10240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, 5264-5271."
doi:10.1002/2014GL060863