Takemasa Miyoshi

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 (2000) in theoretical physics on nonlinear dynamics from the Kyoto University, and M.S. (2004) and Ph.D. (2005) 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 Japan Meteorological Agency (JMA) in 2000. He was a tenure-track Assistant Professor at UMD in 2011. Since 2012, Dr. Miyoshi has been leading the Data Assimilation Research Team in RIKEN Center for Computational Science (R-CCS), working towards advancing the science of data assimilation with a deep commitment to education. Dr. Miyoshi's scientific achievements include more than 140 peer-reviewed publications and more than 180 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, the Yomiuri Gold Medal Prize (2018), and the Commendation by the Prime Minister for Disaster Prevention (2020).

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
  • 2022 'Awards for Science and Technology' Research Category, The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology, April 8, 2022 (科学技術分野の文部科学大臣表彰 科学技術賞 研究部門「数値天気予報を革新するビッグデータ同化の研究」)
  • 2022 RIKEN BAIHO Award (RIKEN Excellent Achievement Award), For FY2021 excellent achievement on Construction of new weather forecast system by large-scale ensemble computations using global cloud resolving model and localized ensemble Kalman filter on Fugaku, March 23, 2022 (理研梅峰賞)
  • 2020 Commendation by the Prime Minister for Disaster Prevention, September 1, 2020 (防災功労者内閣総理大臣表彰)
  • 2018 Excellent Achievement Award, HPCI (High Performance Computing Infrastructure) Project Report Meeting Program Committee, November 2, 2018 (HPCI利用研究課題優秀成果賞)
  • 2018 RIKEN BAIHO Award (RIKEN Excellent Achievement Award), For FY2017 excellent achievement on Research and Development for Research to Fuse Data Analysis with Simulations, June 5, 2018 (理研梅峰賞
  • 2018 Gold Medal Prize, Yomiuri Techno Forum (読売テクノ・フォーラム ゴールド・メダル賞、2018年4月25日「ビッグデータ同化によるゲリラ豪雨予測の研究」)
  • 2017 Editor's Award, Monthly Weather Review, American Meteorological Society
  • 2016 Meteorological Society of Japan Award (2016年度日本気象学会賞
  • 2015 Japan Geoscience Union Nishida Prize 20142014年度地球惑星科学振興西田賞
  • 2014 The Young Scientists' Prize, The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology (科学技術分野の文部科学大臣表彰 若手科学者賞)
  • 2014 Hydraulic Engineering Paper Award, Committee on Hydroscience and Hydraulic Engineering, Japan Society of Civil Engineers (土木学会水工学委員会 水工学論文賞)
  • 2008 Yamamoto-Syono Award, Meteorological Society of Japan (日本気象学会 山本・正野論文賞)
  • 2003-2005 Japanese Government Long-term Fellowship

10 Selected Papers

Complete List of 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