Takemasa Miyoshi
Takemasa Miyoshi
三好 建正

Team Leader
Data Assimilation Research Team
RIKEN Center 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)

Dr. Takemasa Miyoshi received his B.S. degree in theoretical physics from the Kyoto University, and M.S. and Ph.D. degrees in meteorology on ensemble data assimilation from the University of Maryland (UMD). Dr. Takemasa Miyoshi started his professional career as a civil servant at the Japanese Meteorological Agency (JMA). He was a tenure-track Assistant Professor at University of Maryland in 2011. Dr. Miyoshi is now leading the Data Assimilation Research Team in RIKEN Center 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 100 peer-reviewed publications and more than 130 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), the Young Scientists’ Prize by the Minister of Education, Culture, Sports, Science and Technology (2014), the Japan Geosciences Union Nishida Prize (2015), the Meteorological Society of Japan Award (2016) – the highest award of the society, and the Yomiuri Gold Medal Prize (2018).

  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 Center 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

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