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Data Assimilation Research Team

Japanese

Team Leader  Takemasa Miyoshi

  • mail addresstakemasa.miyoshi[at]riken.jp   (Lab location: Kobe)
  • Please change [at] to @
Team WebsiteThe webpage will open in a new tab.
2022
Visiting Professor, Graduate School of Science, Kyoto University, Japan(-present)
2021
Program Director, Japan Science and Technology Agency (JST)(-present)
2018
Deputy Director, RIKEN interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) Program, Japan(-present)
2018
Chief Scientist, RIKEN Cluster for Pioneering Research, Japan(-present)
2018
Affiliate Professor, Graduate School of Science, Kyoto University, Japan
2017
Visiting Principal Scientist, Application Laboratory, JAMSTEC, Japan (-present)
2015
Research Counselor, Servicio Meteorológico Nacional (National Meteorological Service), Argentina(-present)
2014
Visiting Senior Scientist, Application Laboratory, JAMSTEC, Japan
2013
Visiting Senior Research Scientist, Earth Simulator Center, JAMSTEC, Japan
2013
Visiting Professor, University of Maryland, College Park, Maryland, USA(-present)
2012
Team Leader, Data Assimilation Research Team, AICS (renamed R-CCS in 2018), RIKEN (-present)
2011
Assistant Professor, University of Maryland, College Park, Maryland, USA
2009
Research Assistant Professor, University of Maryland, College Park, Maryland, USA
2009
Visiting Assistant Researcher, University of California, Los Angeles, California, USA
2005
Scientific Official, Numerical Prediction Division, Japan Meteorological Agency, Japan
2005
Ph.D. in Meteorology, University of Maryland, College Park, Maryland, USA
2004
M.S. in Meteorology, University of Maryland, College Park, Maryland, USA
2003
Graduate Student, University of Maryland, College Park, Maryland
2002
Scientific Official, Numerical Prediction Division, Japan Meteorological Agency
2000
Technical Official, Planning Division, Japan Meteorological Agency
2000
B.S. in Physics, Faculty of Science, Kyoto University, Kyoto, Japan

Keyword

  • Data Assimilation
  • Numerical Weather Prediction
  • Large Scale Simulation
  • Ensemble Data Assimilation
  • Natural disaster prediction

Research summary

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 results

K computer and advanced weather radar come together to predict sudden torrential rains

Using the K computer, we carried out 100 parallel simulations of a convective weather system, using the nonhydrostatic mesoscale model with 100-meter grid spacing and assimilated data from a next-generation phased-array weather radar, which was implemented in the summer of 2012 by the National Institute of Information and Communications Technology (NICT) and Osaka University. This enabled us to produce a high-resolution three-dimensional distribution map of rain every 30 seconds—120 times more rapidly than the typical hourly updated systems operated at weather prediction centers around the world today.

To test the accuracy of the system, we attempted to model a real case—a sudden storm that took place on September 11, 2014, in Kobe. The simulations were tested without observational data input, as well as with observational data incorporated every 30 seconds on 100-meter and 1-kilometer grid scales. The simulations alone were unable to replicate the rain, while the simulation incorporating observational data enabled the computer to represent the actual storm. In particular, the simulation carried out with 100-meter grids produced an accurate replication of the storm when compared to actual observations.

Distribution of thunderclouds on September 11, 2014
Distribution of thunderclouds on September 11, 2014

Representative papers

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Annual Reports

FY2020（68.1 MB）The pdf will open in a new tab.
FY2019 (2 MB) The pdf will open in a new tab.
Previous reports are available here.

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Predicting sudden downpoursThe webpage will open in a new tab.(YouTube, approx. 2 minutes)

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