RIKEN Center for Computational Science Data Assimilation Research Team

Team Leader: 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. 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.2022.114772
2. 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-29-133-2022
3. Honda, T., A. Amemiya, S. Otsuka, G.-Y. Lien, J. Taylor, Y. Maejima, S. Nishizawa, T. Yamaura, K. Sueki, H. Tomita, S. Satoh, Y. Ishikawa, and T. Miyoshi.:
2022: "Development of the Real-Time 30-s-Update Big Data Assimilation System for Convective Rainfall Prediction with a Phased Array Weather Radar: Description and Preliminary Evaluation"
J. Adv. Modeling Earth Systems, 14(6), e2021MS002823.
doi:10.1029/2021MS002823
4. Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi.:
2019: "GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation. J. Meteor."
Soc. Japan, 97, 1099-1117.
doi:10.2151/jmsj.2019-061
5. 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:10.1002/2017JD027096
6. Arakida, H., T. Miyoshi, T. Ise, S. Shima and S. Kotsuki.:
2017: "Non-Gaussian data assimilation of satellite-based Leaf Area Index observations with an individual-based dynamic global vegetation model."
Nonlinear Processes in Geophys., 24, 553-567.
doi:10.5194/npg-24-553-2017
7. 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.2016.2602560
8. 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
9. Miyoshi, T., K. Kondo, and K. Terasaki.:
2015: "Big Ensemble Data Assimilation in Numerical Weather Prediction."
Computer, 48, 15-21.
doi:10.1109/MC.2015.332
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