2021年業績一覧

査読付原著論文

  1. Tomita, H., M. F. Cronin, and S. Ohishi, 2021: Asymmetric air-sea heat flux response and ocean impact to synoptic-scale atmospheric disturbances observed at JKEO and KEO buoys, Scientific Reports, 11, 469(2021). doi: 10.1038/s41598-020-80665-8
  2. Taylor, J. Honda, T., Amemiya, A., Maejima, Y., and Miyoshi, T., 2020: Predictability of the July 2020 Heavy Rainfall with the SCALE-LETKF, SOLA, 2021, Vol. 17, 48-56.doi:10.2151/sola.2021-008
  3. Xiaoteng Shen, Mingze Lin, Yuliang Zhu, Ho Kyung Ha, Michael Fettwies, Tianfeng Hou, Erik A. Toorman, Jerome P.-Y. Maa, Jinfeng Zhang, A quasi-Monte Carlo based flocculation model for fine-grained cohesive sediments in aquatic environments, Water Research,Volume 194, 15 April 2021, 116953. https://doi.org/10.1016/j.watres.2021.116953
  4. Abdulla Mamun, Yongsheng Chen, Jianyu Liang, 2021: Radiative and cloud microphysical effects of the Saharan dust simulated by the WRF-Chem mode. Journal of Atmospheric and Solar-Terrestrial Physics, Volume 219, 2021, 105646. https://doi.org/10.1016/j.jastp.2021.105646
  5. Honda, T. and T. Miyoshi, 2021: Predictability of the July 2018 Heavy Rain Event in Japan Associated with Typhoon Prapiroon and Southern Convective Disturbances, SOLA, 17, 113-119. https://doi.org/10.2151/sola.2021-018
  6. Taylor, J., A. Okazaki, T. Honda,, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, T. Miyoshi, 2021: Oversampling Reflectivity Observations from a Geostationary Precipitation Radar Satellite: Impact on Typhoon Forecasts within a Perfect Model OSSE Framework. J. Adv. Modeling Earth Systems, Vol. 13, 7. https://doi.org/10.1029/2020MS002332
  7. Richard, S., Sun Q., 2021: Bibliometric analysis on mathematics 3 snapshots: 2005, 2010, 2015. Electronic Journal of Applied Statistical Analysis, Vol. 14, Issue 01, May 2021, 90-116. DOI: 10.1285/i20705948v14n1p90
  8. Yamazaki, A., Miyoshi, T., Inoue, J., Enomoto, T., & Komori, N. (2021). EFSO at Different Geographical Locations Verified with Observing System Experiments, Weather and Forecasting, 36(4), 1219-1236. https://doi.org/10.1175/WAF-D-20-0152.1
  9. Okazaki, A., Miyoshi, T., Yoshimura, K., Greybush, S. J., Zhang, F., Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study, JGR Atmospheres, Volume 126, Issue 16. https://doi.org/10.1029/2020JD034214
  10. Jianyu Liang, Yongsheng Chen, Avelino F. Arellano, and Abdulla Al Mamun, 2021: Model Sensitivity Study of the Direct Radiative Impact of Saharan Dust on the Early Stage of Hurricane Earl. Atmosphere 2021, 12, 1181. https://doi.org/10.3390/atmos12091181
  11. Tomizawa, F. and Y. Sawada (2021), Combining ensemble Kalman filter and reservoir computing to predict spatio-temporal chaotic systems from imperfect observations and models, Geoscientific Model Development, 14, 56235635, 2021. https://doi.org/10.5194/gmd-14-5623-2021
  12. Arakida, H., S. Kotsuki, S. Otsuka, Y. Sawada, and T. Miyoshi, 2021: Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia. Prog Earth Planet Sci., 8, 52. https://doi.org/10.1186/s40645-021-00443-6
  13. Honda, T., Y. Sato, and T. Miyoshi, 2021: Potential impacts of lightning flash observations on numerical weather prediction with explicit lightning processes, Journal of Geophysical Research: Atmospheres, 126. https://doi.org/10.1029/2021JD034611
  14. Dillon, M. E., P. Maldonado, P. Corrales, Y. García Skabar, J. Ruiz, M. Sacco, F. Cutraro, L. Mingari, C. Matsudo, L. Vidal, M. Rugna, M. P. Hobouchian, P. Salio, S. Nesbitt, C. Saulo, E. Kalnay, T. Miyoshi, 2021: A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign. Atmos. Res., Volume 264, 2021. https://doi.org/10.1016/j.atmosres.2021.105858
  15. Ruiz, J., Lien, G.-Y., Kondo, K., Otsuka, S., and Miyoshi, T.: Reduced non-Gaussianity by 30-second rapid update in convective-scale numerical weather prediction, Nonlin. Processes Geophys. https://doi.org/10.5194/npg-2021-15

招待講演

  1. Takemasa Miyoshi, T. Honda, A. Amemiya, S. Otsuka, Y. Maejima, J. Taylor, H. Tomita, S. Nishizawa, K. Sueki, T. Yamaura, Y. Ishikawa, S. Satoh, T. Ushio, K. Koike, E. Hoshi, and K. Nakajima, Big Data Assimilation: Real-Time Demonstration Experiment of 30-s-Update Forecasting in Tokyo in August 2020, American Meteorological Society 101st Annual meeting, online, January 11, 2021.
  2. 三好建正、ビッグデータ同化 ゲリラ豪雨予測から、予測科学へ、JST/CRDSセミナー『数学と科学、⼯学の協働に関する連続セミナー 』第13回「シミュレーションとデータ科学」、オンライン、2021年1月27日.
  3. Takemasa Miyoshi, Big Data, Big Computation, and Machine Learning in Numerical Weather Prediction, AI Chair OceaniX Webinars, IMT-Atlantique & RIKEN Online Joint Seminar Series (Jointly with Data Assimilation Seminar Series), February 17, 2021.
  4. 三好建正、データ同化と気象予測の展望、JST未来社会創造事業ワークショップ、｢次世代情報社会の実現｣領域 R03重点公募テーマ検討ワークショップ、オンライン、2021年2月23日.
  5. Takemasa Miyoshi, Fusing Big Data and Big Computation in Numerical Weather Prediction, Climate Research with HPC Forum, SupercomputingAsia 2021, online, March 4, 2021.
  6. Serge RICHARD, Qiwen SUN, Bibliometric analysis on mathematics, 3 snapshots: 2005, 2010, 2015, Himeji conference on partial differential equations, online, March 5, 2021.
  7. 寺崎康児、三好建正、全球水平解像度56km・1024メンバーのNICAM-LETKFを用いた令和2年7月豪雨実験、第54回メソ気象研究会、オンライン、2021年3月8日.
  8. Takemasa Miyoshi, Weather Predictability and Data Assimilation: Perspectives Toward General Theory of Prediction, OIST-RIKEN Collaboration 1st Symposium: Green and blue planet -How can ecological research shape our future?, Okinawa, April 6, 2021.
  9. 三好建正、ビッグデータ同化：ゲリラ豪雨予測から予測科学へ, 進化型共生システムシンポジウム-未来の機械工学を考える-、オンライン 、2021年4月12日.
  10. P. Tandeo, P. Ailliot, C. Bello, B. Chapron, T. Chau, R. Knutti, P. Le Bras, P. Naveau, V. Monbet, J. Ruiz, F. S'evellec, A.-M. Treguier, Narrowing uncertainties in climate projections using data science tools, 3rd joint RIKEN & IMT-Atlantique workshop on Statistical Modeling and Machine Learning in Meteorology and Oceanography, online, April 14, 2021.
  11. Takemasa Miyoshi, Seminar "Fusing Big Data and Big Computation in Numerical Weather Prediction", Fluid Mechanics Unit (Professor Pinaki Chakraborty) Seminar, Online, April 20, 2021.
  12. Takemasa Miyoshi, Big Data, Big Computation, and Machine Learning in Numerical Weather Prediction, 14th International Conference on Mesoscale Convective Systems and High-Impact Weather in East Asia (ICMCS-XIV), Online, April 30, 2021.
  13. Kotsuki, S., Terasaki, K., Satoh, M., and Miyoshi, T.: Ensemble-Based Data Assimilation of GPM DPR Reflectivity into the Nonhydrostatic Icosahedral Atmospheric Model NICAM. JpGU 2021, Online, June 3, 2021.
  14. 前島康光, 川畑拓矢, 瀬古弘, 三好建正: 令和2年7月豪雨を対象とした高密度PAWRの観測システムシミュレーション実験, 「富岳」成果創出加速プログラムサブテーマ2ミーティング, オンライン, 2021年6月25日.
  15. Amemiya, A., T. Miyoshi, 1000-member 18-km-mesh SCALE-LETKF experiment with conventional observations in summer 2020, Asia Oceania Geosciences Society 18th Annual meeting, Online, August 4, 2021.
  16. Miyoshi Takemasa, Fusing Big Data and Big Computation in Numerical Weather Prediction, Data-Centric Engineering Webinar Series, Online, November 3, 2021.
  17. 古川賢, プリミティブ方程式とNavier-Stokes方程式に関連する特異極限問題について, 九州関数方程式セミナー, オンライン、2021年11月5日.
  18. 三好建正、ビッグデータ同化:ゲリラ豪雨予測から予測科学へ、2021年度パナソニック総合技術シンポジウム(第39回)専門技術セッション、大阪、2021年12月3日.
  19. 三好建正、数値天気予報の要:データ同化とその展望、第60回玉城嘉十郎教授記念公開学術講演会、京都大学、2021年12月20日.

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