2023年業績一覧

査読付原著論文

  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. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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.
  17. 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.
  18. 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.

招待講演

  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. Takemasa Miyoshi, Chaos implies effective controllability of extreme weather, The Third International Nonlinear Dynamics Conference (NODYCON 2023), Rome, Italy, June 19, 2023, Keynote
  8. 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

受賞

  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

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