Takemasa Miyoshi
Takemasa Miyoshi
三好 建正

Team Leader
Data Assimilation Research Team
RIKEN Advanced Institute for Computational Science
(Current CV)

What's New

Research Interests (more details)

Data assimilation with chaotic dynamical systems such as the weather system. Predictability, control, and synchronization of chaos. Improving numerical weather prediction (NWP) through data assimilation with particular focus on high-impact weather including Tropical Cyclones (Hurricanes and Typhoons).

  • Theoretical development on data assimilation, including an algorithmic design for efficient computations and methodological development for nonlinear, non-Gaussian applications
  • Pioneering new applications of data assimilation for various high-performance-computer simulations
  • Advancing data assimilation for making sense of "Big Data"
  • Local Ensemble Transform Kalman Filter (LETKF)

Biographical Sketch (Complete CV)

Upon completing the B.S. degree in theoretical physics from the Kyoto University in 2000, Dr. Takemasa Miyoshi started his professional career as a civil servant at the Japanese Meteorological Agency (JMA). After two years of administrative experience, Dr. Miyoshi started his scientific career on numerical weather prediction and developed the three-dimensional variational data assimilation system from scratch for the JMA nonhydrostatic mesoscale model. In 2003, Dr. Miyoshi received a Japanese government fellowship to study at the University of Maryland (UMD) and completed both M.S. and Ph.D. degrees in meteorology on ensemble data assimilation within two years. In 2005, Dr. Miyoshi moved back to JMA and was in charge of developing the JMA's next generation ensemble data assimilation systems. During the four years at JMA, Dr. Miyoshi came to be recognized as a leading scientist in data assimilation; he was asked to give invited talks at several international conferences and to be a member of the organizing committee of the World Meteorological Organization's data assimilation symposium in Melbourne, the most prestigious conference in the field. In 2009, Dr. Miyoshi moved to UMD as a Research Assistant Professor, and got deeply involved in education as a tenure-track Assistant Professor since 2011. In 2012, Dr. Miyoshi started leading the Data Assimilation Research Team in RIKEN Advanced Institute for Computational Science, and has been working towards his goals of advancing the science of data assimilation as well as a deep commitment to education. Dr. Miyoshi’s scientific achievements include more than 70 peer-reviewed publications and more than 50 invited conference presentations including the Core Science Keynote at the American Meteorological Society Annual Meeting (2015). Dr. Miyoshi has been recognized by several prestigious awards such as the Yamamoto-Syono Award by the Meteorological Society of Japan (2008) and the Young Scientists’ Prize by the Minister of Education, Culture, Sports, Science and Technology (2014), the Japan Geosciences Union Nishida Prize (2015), and the Meteorological Society of Japan Award (2016).

  1. Degrees
    • 2005 Ph.D. in Meteorology, University of Maryland, College Park, Maryland
    • 2004 M.S. in Meteorology, University of Maryland, College Park, Maryland
    • 2000 B.S. in Physics, Faculty of Science, Kyoto University, Kyoto, Japan
  2. Positions
    • 2012-present Team Leader, Data Assimilation Research Team, RIKEN Advanced Institute for Computational Science
    • 2011-2012 Assistant Professor, University of Maryland, College Park, Maryland
    • 2009-2011 Research Assistant Professor, University of Maryland, College Park, Maryland
    • 2009 Visiting Assistant Researcher, University of California, Los Angeles, California
    • 2005-2008 Scientific Official, Numerical Prediction Division, Japan Meteorological Agency
    • 2003-2005 Graduate Student, University of Maryland, College Park, Maryland
    • 2002-2003 Scientific Official, Numerical Prediction Division, Japan Meteorological Agency
    • 2000-2002 Technical Official, Planning Division, Japan Meteorological Agency
  3. Awards

10 Selected Papers (Complete List of Publications)

  1. 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
  2. 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., 1347-1354. doi:10.1175/BAMS-D-15-00144.1
  3. 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
  4. Miyoshi, T., K. Kondo, and T. Imamura, 2014: The 10240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, doi:10.1002/2014GL060863.
  5. Miyoshi, T. and K. Kondo, 2013: A multi-scale localization approach to an ensemble Kalman filter. SOLA, 9, 170-173. doi:10.2151/sola.2013-038
  6. Miyoshi, T., E. Kalnay, and H. Li, 2013: Estimating and including observation-error correlations in data assimilation. Inverse Problems in Science and Engineering, 21, 387-398. doi:10.1080/17415977.2012.712527
  7. Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333. doi:10.1007/s00024-011-0373-4
  8. Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535. doi:10.1175/2010MWR3570.1
  9. Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866. doi:10.1175/2010MWR3209.1
  10. Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861. doi:10.1175/2007MWR1873.1