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RIKEN Center for Computational Science Data Assimilation Research Team

Team Leader: Takemasa Miyoshi (Ph.D.)

Research Summary

Takemasa  Miyoshi(Ph.D.)

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


  • 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. Kotsuki S., Y. Sato, and T. Miyoshi.:
    "Data Assimilation for Climate Research: Model Parameter Estimation of Large Scale Condensation Scheme"
    J. Geophys. Res., 125, e2019JD031304 (2020).
  • 2. Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi.:
    "GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation"
    J. Meteor. Soc. Japan, 97, 1099-1117 (2019).
  • 3. Kondo, K., and T. Miyoshi.:
    "Non-Gaussian statistics in global atmospheric dynamics: a study with a 10240-member ensemble Kalman filter using an intermediate AGCM"
    Nonlinear Processes in Geophys., 26, 211-225 (2019).
  • 4. Honda, T., S. Kotsuki, G.-Y. Lien, Y. Maejima, K. Okamoto and T. Miyoshi.:
    "Assimilation of Himawari-8 All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction"
    J. Geophys. Res., 123, 965-976 (2018).
  • 5. Kotsuki, S., T. Miyoshi, K. Terasaki, G.-Y. Lien and E. Kalnay.:
    "Assimilating the Global Satellite Mapping of Precipitation Data with the Nonhydrostatic Icosahedral Atmospheric Model NICAM"
    J. Geophys. Res., 122, 631-650 (2017).
  • 6. Arakida, H., T. Miyoshi, T. Ise, S. Shima and S. Kotsuki.:
    "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 (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.:
    ""Big Data Assimilation" toward Post-peta-scale Severe Weather Prediction: An Overview and Progress"
    Proc. of the IEEE, 104, 2155-2179 (2016).
  • 8. Miyoshi, T., M. Kunii, J. Ruiz, G.-Y. Lien, S. Satoh, T. Ushio, K. Bessho, H. Seko, H. Tomita, and Y. Ishikawa.:
    ""Big Data Assimilation" Revolutionizing Severe Weather Prediction"
    Bull. Amer. Meteor. Soc., 97, 1347-1354 (2016).
  • 9. Miyoshi, T., K. Kondo, and K. Terasaki.:
    "Big Ensemble Data Assimilation in Numerical Weather Prediction"
    Computer, 48, 15-21 (2015).
  • 10. Miyoshi, T., K. Kondo, and T. Imamura.:
    "The 10240-member ensemble Kalman filtering with an intermediate AGCM"
    Geophys. Res. Lett., 41, 5264-5271 (2014).

Related Links

Lab Members

Principal investigator

Takemasa Miyoshi
Team Leader

Core members

Koji Terasaki
Research Scientist
Shigenori Otsuka
Research Scientist
Takumi Honda
Special Postdoctoral Researcher
Kohei Takatama
Postdoctoral Researcher
James Taylor
Postdoctoral Researcher
Arata Amemiya
Postdoctoral Researcher
Maha Mdini
Postdoctoral Researcher
Shun Ohishi
Postdoctoral Researcher
Yasumitsu Maejima
Postdoctoral Researcher
Jianyu Liang
Postdoctoral Researcher
Qiwen Sun
Junior Research Associate
Hideyuki Sakamoto
Technical Staff I
John Craig Wells
Senior Visiting Scientist
Shu-Chih Yang
Senior Visiting Scientist
Juan Ruiz
Visiting Scientist
Yohei Sawada
Visiting Scientist
Pierre Francois Yves Tandeo
Visiting Scientist
Keiichi Kondo
Visiting Scientist
Atsushi Okazaki
Visiting Scientist
Shunji Kotsuki
Visiting Scientist
Hazuki Arakida
Visiting Scientist
Hironori Arai
Visiting Scientist
Tobias Marcel Necker
Visiting Scientist


Position Deadline
Seeking a Research Scientist or a Senior Research Scientist (20K69) Dec 8, 2020
Seeking Senior Scientist/Research Scientist/Postdoctoral Researcher Open until filled

Contact Information

Email: da-team-desk [at] ml.riken.jp