Centers & Labs

RIKEN Advanced Institute for Computational Science

Data Assimilation Research Team

Team Leader: Takemasa Miyoshi (Ph.D.)
Takemasa  Miyoshi(Ph.D.)

Data assimilation is a cross-disciplinary science to synergize numerical simulations and real-world data, based on statistical mathematics and dynamical systems theory. As computers become more powerful and enable more precise simulations, it will become more important to compare the simulation with real-world observations. Data Assimilation Research Team ("DA team") performs cutting-edge research and development on advanced data assimilation methods and their wide applications, aiming to integrate computer simulations and observational data in the wisest way. Particularly, the DA team will tackle challenging problems of developing efficient and accurate data assimilation systems for high-dimensional simulations with “Big Data”

Main Research Field


Related Research Fields

Physics / Engineering / Mathematics / Environment & Ecology / Geosciences

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. Lien, G.-Y., Miyoshi, T., and Kalnay, E.:
    “Assimilation of TRMM Multisatellite Precipitation Analysis with a low-resolution NCEP Global Forecasting System”
    Mon. Wea. Rev., 144, 643-661 (2016)
  2. Miyoshi, T., Kondo, K., and Terasaki, K.:
    “Big Ensemble Data Assimilation in Numerical Weather Prediction”
    Computer, 48, 15-21 (2015)
  3. Miyoshi, T., Kondo, K., and Imamura, T.:
    “The 10,240-member ensemble Kalman filtering with an intermediate AGCM”
    Geophys. Res. Lett., 41, 5264–5271 (2014)
  4. Terasaki, K., and Miyoshi, T.:
    “Data Assimilation with Error-correlated and Non-orthogonal Observations: Experiments with the Lorenz-96 Model”
    SOLA, 10, 210-213 (2014)
  5. Miyoshi, T., and Kondo, K.:
    “A multi-scale localization approach to an ensemble Kalman filter”
    SOLA, 9, 170-173 (2013)
  6. Ruiz, J. J., Pulido, M., and Miyoshi, T.:
    “Estimating model parameters with ensemble-based data assimilation: A review”
    J. Meteorol. Soc. Japan., 91, 79-99 (2013)
  7. Miyoshi, T., Kalnay, E., and Li, H.:
    “Estimating and including observation-error correlations in data assimilation”
    Inverse Problems in Science and Engineering, 21, 387-398 (2013)
  8. Miyoshi, T., and Kunii, M.:
    “The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations”
    Pure and Appl. Geophys., 169, 321-333 (2012)
  9. Miyoshi, T.:
    “The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter”
    Mon. Wea. Rev., 139, 1519-1535 (2011)
  10. Miyoshi, T., and Yamane, S.:
    “Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution”
    Mon. Wea. Rev., 135, 3841-3861 (2007)

Lab Members

Principal Investigator

Takemasa Miyoshi
Team Leader

Core Members

Koji Terasaki
Research Scientist
Shigenori Otsuka
Postdoctoral Researcher
Keiichi Kondo
Postdoctoral Researcher
Shunji Kotsuki
Postdoctoral Researcher
Guo-Yuan Lien
Postdoctoral Researcher
Takumi Honda
Postdoctoral Researcher
Yohei Sawada
Postdoctoral Researcher
Atsushi Okazaki
Postdoctoral Researcher
Toshiki Teramura
Postdoctoral Researcher
Yasumitsu Maejima
Research Associate
Hazuki Arakida
Technical Staff I
Taeka Awazu
Technical Staff I
Juan Jose Ruiz
Visiting Scientist
Shinichiro Shima
Visiting Scientist
Shu-Chih Yang
Visiting Scientist
Stephen Penny
Visiting Scientist
Masaru Kunii
Visiting Scientist
Michiko Otsuka
Visiting Scientist
Kozo Okamoto
Visiting Scientist