1. Miyoshi, T., A. Amemiya, S. Otsuka, Y. Maejima, J. Taylor, T. Honda, H. Tomita, S. Nishizawa, K. Sueki, T. Yamaura, Y. Ishikawa, S. Satoh, T. Ushio, K. Koike, and A. Uno, 2023: Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, 8, 1-10. doi:10.1145/3581784.3627047
  2. Mulia, I. E., N. Ueda, T. Miyoshi, T. Iwamoto, M. Heidarzadeh, 2023: A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Scientific Reports., doi: 10.1038/s41598-023-35093-9
  3. Sun, Q., T. Miyoshi, and S. Richard, 2023: Control Simulation Experiments of Extreme Events with the Lorenz-96 Model. Nonlin. Processes Geophys., 30, 117-128. https://doi.org/10.5194/npg-30-117-2023
  4. 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
  5. Taylor, J., T. Honda, A. Amemiya, Otsuka, S., Maejima, Y. and T. Miyoshi, 2023: Sensitivity to Localization Radii for an Ensemble Filter Numerical Weather Prediction System with 30-Second Update. Weather and Forecasting, 38, 611-632. doi:10.1175/WAF-D-21-0177.1
  6. R. T. Konduru et al., 2023: Unravelling the causes of 2015 winter monsoon extreme rainfall and floods over Chennai: Influence of atmospheric variability and urbanization on the hydrological cycle. Urban Climate, 47, 101395., doi:10.1016/j.uclim.2022.101395
  7. Necker, T., Hinger, D., Griewank, P. J., Miyoshi, T., and Weissmann, M., 2023: Guidance on how to improve vertical covariance localization based on a 1000-member ensemble. Nonlin. Processes Geophys., 30, 13-29., https://doi.org/10.5194/npg-30-13-2023
  8. Liang, J., K. Terasaki, and T. Miyoshi, 2023: A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation. J. Meteor. Soc. Japan., 101, 79-95., https://doi.org/10.2151/jmsj.2023-005
  9. Amemiya, A., M. Shlok, T. Miyoshi, 2023: Application of recurrent neural networks to model bias correction: Idealized experiments with the Lorenz-96 model. Journal of Advances in Modeling Earth Systems, 15(2), e2022MS003164., https://doi.org/10.1029/2022ms003164
  10. Takemasa Miyoshi, Chaos implies effective controllability of extreme weather, The Third International Nonlinear Dynamics Conference (NODYCON 2023), Rome, Italy, June 19, 2023, Keynote
  11. Ohishi, S., T. Miyoshi, and M. Kachi, 2023: LORA: A local ensemble transform Kalman filter-based ocean research analysis. Ocean Dynamics, 73, 117-143., https://doi.org/10.1007/s10236-023-01541-3
  12. Yamazaki, A., K. Terasaki, T. Miyoshi, and S. Noguchi, 2023: Estimation of AMSU-A Radiance Observation Impacts in an LETKF-Based Atmospheric Global Data Assimilation System: Comparison with EFSO and Observing System Experiments. Weather and Forecasting, 38, 953-970. doi:10.1175/WAF-D-22-0159.1
  13. Kotsuki, S., K. Tetasaki, M. Satoh, and T. Miyoshi, 2023: Ensemble-based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). Journal of Geophysical Research: Atmospheres, 128, e2022JD037447. doi:10.1029/2022JD037447
  14. Nomokonova, T., P. Griewank, U. Loehnert, T. Miyoshi, T. Necker, and M. Weissmann, 2023: Estimating the benefit of Doppler wind lidars for short-term low-level wind ensemble forecasts. QJRMS., 149, 192-210. doi:10.1002/qj.4402
  15. Kurosawa, K., S. Kotsuki, and T. Miyoshi, 2023: Comparative Study of Strongly and Weakly Coupled Soil Moisture Data Assimilation with a Global Coupled Land-Atmosphere Model. Nonlin. Processes Geophys., 30, 457-479. doi:10.5194/npg-30-457-2023
  16. Honda, T., Y. Sato, and T. Miyoshi, 2023: Regression-based ensemble perturbations for the zero-gradient issue posed in lightning-flash data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 151, 2573-2586. doi:10.1175/MWR-D-22-0334.1
  17. Otsuka, S., T. Awazu, C. A. Welzbacher, R. Potthast, and T. Miyoshi, 2023: Assimilating Precipitation Features Based on the Fractions Skill Score: An Idealized Study with an Intermediate AGCM. Numerical Weather Prediction: East Asian Perspectives, Springer, 283-294. doi:10.1007/978-3-031-40567-9_11
  18. Saito, K., T. Kawabata, H. Seko, T. Miyoshi, L. Duc, T. Oizumi, M. Kunii, G. Chen, K. Ito, J. Ito, S. Yokota, W. Mashiko, K. Kobayashi, S. Fukui, E. Tochimoto, A. Amemiya, Y. Maejima, T. Honda, H. Niino, and M. Satoh, 2023: Forecast and Numerical Simulation Studies on Meso/Micro-scale High-Impact Weathers Using High-Performance Computing in Japan. Numerical Weather Prediction: East Asian Perspectives, Springer, 461-481. doi:10.1007/978-3-031-40567-9_18
  19. Takatama, K., J. C. Wells, Y. Uchiyama, T. Miyoshi, 2023: Simulating Rapid Water Level Decrease of Lake Biwa Due to Typhoon Jebi (2018). Numerical Weather Prediction: East Asian Perspectives, Springer, 559-567. doi:10.1007/978-3-031-40567-9_21
  20. Oishi, K., and S. Kotsuki, 2023: Applying the Sinkhorn Algorithm for Resamling of Local Particle Filter. SOLA, 19, 185-193., doi:10.2151/sola.2023-024
  21. Muto, Y., K. Kanemaru, and S. Kotsuki, 2023: Correcting GSMaP through Histogram Matching against Satellite-Borne Radar-Based Precipitation. SOLA, 19, 217-224., doi:10.2151/sola.2023-028
  22. Takeda, K. and T. Sakajo, 2023: Geometric Ergodicity for Hamiltonian Monte Carlo on Compact Manifolds. SIAM Journal on Numerical Analysis, 61(6), 2994-3013. doi:10.1137/22M1543550


  1. Konduru Rakesh Teja, How to make high resolution simulations representative of future climate?, Examining the impact of Aerosol, Urbanization, and Irrigation on extreme rainfall occurrences over India using Cloud - Resolving Simulations, Indian Institute of Technology Madras, India, January 28, 2023
  2. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku DA Forum by University of Melbourne, Melbourne, Australia, February 3, 2023
  3. 前島康光、領域気象モデルを用いた局地豪雨の制御シミュレーション実験、第14回理研・京大データ同化研究会、神戸、2023年2月15日
  4. Konduru Rakesh Teja, Jun Matsumoto, Masato I. Nodzu, and Yoshiyuki Kajikawa, Ubiquitous nature of the diurnal cycle of precipitation and its representation in current generation climate models. International workshops on climate, water, land, and life in a monsoon Asia, hosted by Tokyo Metropolitan University, Minami-Osawa, Tokyo, March 6, 2023
  5. 前島康光、富岳を用いた2021年夏季リアルタイムゲリラ豪雨予測の結果解析、富岳成果創生加速課題成果報告会、東京大学、2023年3月20日
  6. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, Data Assimilation forum at Centre for Climate Research Singapore (CCRC) Seminar, Online, April 12, 2023
  7. 三好建正、詳細非公開、2023年4月21日
  8. 三好建正、気象災害に脆弱な人口密集地域のための数値天気予報と防災情報提供システムのプロジェクト(対象国:アルゼンチン)、日本気象学会 第2回国際協力研究連絡会、オンライン、2023年5月17日
  9. 三好建正、ゲリラ豪雨予測から予測科学へ、南部コロキウム、大阪大学大学院理学研究科、2023年6月1日
  10. Konduru Rakesh Teja, Seamless predictability of rainfall systems by employing ultra-high resolution computational simulations and their applications. at Department of Civil Engineering, National Institute of Technology Warangal, India, June 6, 2023
  11. Konduru, R.T., Liang, J. and Miyoshi, T. Challenges in assimilating high-frequency satellite observations and diagnosing high-frequency errors: Insights from global NICAM-LETKF system. at Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India, June 7, 2023
  12. Otsuka, S. and T. Miyoshi, Development of precipitation nowcasting systems at RIKEN and Japan-Argentina cooperation project. Bi-annual meeting of the Nowcasting and Mesoscale Research Working Group, Seoul, Korea, June 26, 2023
  13. Takemasa Miyoshi, Chaos implies effective controllability of extreme weather, The Third International Nonlinear Dynamics Conference (NODYCON 2023), Rome, Italy, June 19, 2023, Keynote
  14. 三好建正、ゲリラ豪雨予測から予測科学へ、株式会社経営戦略研究所 定期研修会、TCATホール(東京・水天宮)、2023年7月4日
  15. 三好建正、詳細非公開、2023年7月10日
  16. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, The 28th IUGG General Assembly (IUGG2023), Berlin, July 14, 2023
  17. 大塚成徳、最先端のスーパーコンピュータを用いた天気予報研究、日本気象学会 第57回夏季大学 新しい気象学2023、オンライン、2023年8月5日
  18. 竹田航太、坂上貴之、Computing the invariant measure of the N-vortex problem on the sphere by Hamiltonian Monte Carlo、ICIAM、Waseda University、2023年8月22日
  19. Takemasa Miyoshi, Moonshot Goal 8 Realization of a society safe from the threat of extreme winds and rains by controlling and modifying the weather by 2050, International Symposium on Theory of Weather Controllability, Kobe, August 28, 2023, Keynote
  20. 竹田航太、谷地村敏明、Ensemble filter with the optimal transport of gaussian mixture distributions、IMT-Atlantique & Kyoto University & RIKEN joint Data Assimilation workshop、R-CCS、2023年8月29日
  21. Paula Maldonado, Juan Ruiz, Celeste Saulo, Takumi Honda and Takemasa Miyoshi, Assimilation of C-Band Radar Data Using the SCALE-LETKF system: A supercell case study during the RELAMPAGO field campaign, The 6th International Workshop on Nonhydrostatic Models, Sapporo, September 1, 2023
  22. Sebastián López, María Eugenia Dillon, Paula Maldonado, Arata Amemiya, Juan Ruiz, Yanina García Sckabar, Shigenori Otsuka, Kaiki Kakinuma, Kentaro Aida, Tomoki Ushiyama, Maite Cancelada, Daichi Kitahara, Martín Rugna, Mariano Re, Carlos Marcelo García, Celeste Saulo, Takemasa Miyoshi: Numerical Weather Prediction performance assessment using a distributed hydrological model. The 6th International Workshop on Nonhydrostatic Models, Sapporo, September 1, 2023
  23. 大塚成徳、深層学習を用いた降水ナウキャスト、第9回メソ気象セミナー、高知、2023年9月23日
  24. 雨宮新、30秒更新の超高頻度データ同化による数値予測、第9回メソ気象セミナー、高知、2023年9月23日
  25. 三好建正、詳細非公開、2023年11月8日
  26. 古川賢、いくつかの力学系とそのデータ同化による予測について、数値解析セミナー、東京、2023年11月14日
  27. 大石俊三好建正、可知美佐子、アンサンブル海洋データ同化システムの開発、第60回日本航空宇宙学会関西・中部支部合同秋季大会、大阪、2023年11月25日
  28. 古川賢、北畑 裕之、アクアリウムに対するシンプルな濾過のモデルと動的境界条件付き移流拡散方程式について、数理解析若手交流会、オンライン、2023年12月9日
  29. Lin Li, Takemasa Miyoshi, Towards a typhoon-threat-free society in 2050 by weather modification, Mini Symposium for Sakura Plan 2023, Nara, Japan, December 24, 2023


  1. Yasumitsu Maejima:日本気象学会SOLA論文賞, Takuya Kawabata, Hiromu Seko, and Takemasa Miyoshi 「Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event」 2023年2月
  2. 大石俊:理研桜舞賞(研究奨励賞)「アンサンブルカルマンフィルタに基づく海洋データ同化システムの開発」(Development of an ensemble Kalman filter-based ocean data assimilation system)2023年3月22日
  3. Iyan Mulia:理研桜舞賞(研究奨励賞)「Machine learning-based tsunami inundation prediction」2023年3月22日
  4. 三好建正:理研栄峰賞「気象学におけるビッグデータ同化及び制御可能性を切り拓く研究」(Pioneering Meteorological research on big data assimilation and controllability)2023年3月22日


E-mail: da-team-desk(please remove here)@ml.riken.jp


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E-mail: da-team-desk(please remove here)@ml.riken.jp