2020年業績一覧

査読付原著論文

  1. Miyoshi, T., S. Kotsuki, K. Terasaki, S. Otsuka, G.-Y. Lien, H. Yashiro, H. Tomita, M. Satoh, and E. Kalnay, 2020: Precipitation Ensemble Data Assimilation in NWP Models. In: Levizzani V., Kidd C., Kirschbaum D., Kummerow C., Nakamura K., Turk F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, 69, Springer, 983-991. doi:10.1007/978-3-030-35798-6_25
  2. Chang, C., S. G. Penny, and S. Yang, 2020: Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble. Mon. Wea. Rev., 148, 2331–2350. https://doi.org/10.1175/MWR-D-19-0128.1
  3. Hsiang-Wen Cheng, Shu-Chih Yang, Yu-Chieng Liou, Ching-Sen Chen, 2020: An Investigation of the Sensitivity of Predicting a Severe Rainfall Event in Northern Taiwan to the Upstream Condition with a WRF-based Radar Data Assimilation System, SOLA, 2020, Volume 16, Pages 97-103
  4. Wu, P., S. Yang, C. Tsai, and H. Cheng, 2020: Convective-Scale Sampling Error and Its Impact on the Ensemble Radar Data Assimilation System: A Case Study of a Heavy Rainfall Event on 16 June 2008 in Taiwan. Mon. Wea. Rev., 148, 3631–3652. https://doi.org/10.1175/MWR-D-19-0319.1.
  5. Tandeo, P., P. Ailliot, M. Bocquet, A. Carrassi, T. Miyoshi, M. Pulido, and Y. Zhen, 2020: A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation. Mon. Wea. Rev., 148, 3973–3994. https://doi.org/10.1175/MWR-D-19-0240.1
  6. Sawada, Y., 2020: Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model, Journal of Geophysical Research - Atmospheres, 125, Issue20, e2020JD032688. https://doi.org/10.1029/2020JD032688
  7. Kotsuki, S., Pensoneault, A., Okazaki, A. and Miyoshi, T., 2020: Weight Structure of the Local Ensemble Transform Kalman Filter: A Case with an Intermediate AGCM., Q. J. R. Meteorol. Soc., 146, Issue732, 3399-3415. doi:10.1002/qj.3852
  8. H. Yashiro, K. Terasaki, Y. Kawai, S. Kudo, T. Miyoshi, T. Imamura, K. Minami, H. Inoue, T. Nishiki, T. Saji, M. Satoh, and H. Tomita, 2020: A 1024-Member Ensemble Data Assimilation with 3.5-Km Mesh Global Weather Simulations, in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Atlanta, GA, US, 2020 pp. 1-10. doi: 10.1109/SC41405.2020.00005
  9. Amemiya, A. and Sato, K., 2020: Characterizing quasi-biweekly variability of the Asian monsoon anticyclone using potential vorticity and large-scale geopotential height field, Atmospheric Chemistry and Physics, 20, 13857–13876, 2020. https://doi.org/10.5194/acp-2020-424
  10. 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
  11. 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

招待講演

  1. Kotsuki, S., Miyoshi, T., Kondo, K. and Potthast, R.: A Local Particle Filter and Its Gaussian Mixture Extension: Experiments with an Intermediate AGCM. RIKEN Data Assimilation Seminar, online, September 11, 2020.
  2. Keiichi Kondo, Shunji Kotsuki, Takemasa Miyoshi, A local particle filter based on non-Gaussian statistics using an intermediate AGCM, DA seminar, online, September 11, 2020.
  3. Takemasa Miyoshi, Big Data, Big Computation, and Machine Learning in Numerical Weather Prediction, Workshop on Data Assimilation and Uncertainty Quantification at the exascale, online, September 24, 2020.
  4. Takemasa Miyoshi, Big Data, Big Computation, and Machine Learning in Numerical Weather Prediction,Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, online, October 6, 2020.
  5. 三好 建正、ビッグデータとスーパーコンピュータによる豪雨予測 -世界最先端「ビッグデータ同化」の気象予測研究-、第25回日本難病看護学会 第8回日本難病医療ネットワーク学会合同学術集会、オンライン、2020年11月20日
  6. Takemasa Miyoshi, Predicting Sudden Local Storms by 30-second-update NWP Using Phased Array Weather Radar, KU-ITB Biweekly Webinar Series, online, November 27, 2020.
  7. 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 12, 2021.
  8. 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.
  9. 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.
  10. 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.
  11. Takemasa Miyoshi, Fusing Big Data and Big Computation in Numerical Weather Prediction, Climate Research with HPC Forum, SupercomputingAsia 2021, online, March 4, 2021.
  12. Serge RICHARD, Qiwen SUN, Bibliometric analysis on mathematics, 3 snapshots: 2005, 2010, 2015, Himeji conference on partial differential equations, online, March 5, 2021.
  13. 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

受賞

  1. 大石俊: 日本海洋学会奨励論文賞「Frontolysis by surface heat flux in the eastern Japan Sea: importance of mixed layer depth」 2020年11月28日.
  2. 三好建正: 令和2年度防災功労者内閣総理大臣表彰. 2020年9月1日.
  3. 大塚成徳: 令和2年度理化学研究所 桜舞賞 「新しい 3D 降水ナウキャスト手法の開発とリアルタイム実証」 2020年4月3日.

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