Achievements in 2022

Peer-reviewed papers

  1. Mulia, I.E, N. Ueda, T. Miyoshi, A.R. Gusman, K. Satake, 2022: Machine learning-based tsunami inundation prediction derived from offshore observations., Nat Commun., 13, 5489. doi.org/10.1038/s41467-022-33253-5
  2. Otsuka, S., 2022: Visualizing Lamb waves from a volcanic eruption using meteorological satellite Himawari-8., Geophys. Res. Lett., 49. e2022GL098324
  3. Konduru et al., 2022: Climatological characteristics of nocturnal eastward-propagating diurnal precipitation peak over South India during summer monsoon: Role of monsoon low-level circulation and gravity waves., Meteorological Applications., 29(6), doi:10.1002/met.2106
  4. Ohishi, S., T. Miyoshi, and M. Kachi, 2022: An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI)., Geosci. Model Dev., 15, 9057-9073, doi:10.5194/gmd-15-9057-2022
  5. Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, M. Kachi, and T. Miyoshi, 2022: An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0., Geosci. Model Dev., 15, 8395-8410, doi:10.5194/gmd-15-8395-2022.
  6. Kameyama, K., Y. Kanno, S. Ohishi, H. Tomita, Y. Fukutomi, and H. Aiki, 2022: Sporadic low salinity signals in the oceanic mixed layer observed by the Kuroshio Extension Observatory buoy., Frontiers in Climate., 4:820490, doi:10.3389/fclim.2022.820490
  7. Sakajo, T., S. Ohishi, and T Uda, 2022: Identification of Kuroshio meanderings south of Japan via a topological data analysis of sea surface height, Journal of Oceanography., 78, 495-513, doi:10.1007/s10872-022-00656-3
  8. Fujisawa, Y., Sy. Murakami, N. Sugimoto, M. Takagi, T. Imamura, T. Horinouchi, G L. Hashimoto, M. Ishiwatari, T. Enomoto, T. Miyoshi, H. Kashimura, Y-Y. Hayashi, 2022: The first assimilation of Akatsuki single-layer winds and its validation with Venusian atmospheric waves excited by solar heating., Sci Rep 12, 14577. doi.org/10.1038/s41598-022-18634-6
  9. Craig, G, M. Puh, C. Keil, K. Tempest, T. Necker, J. Ruiz, M. Weissmann, T. Miyoshi, 2022: Distributions and convergence of forecast variables in a 1000 member convection-permitting ensemble, Quart. J. Roy. Meteorol. Soc. , 148(746), 2325-2343. doi.org/10.1002/qj.4305
  10. 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
  11. Honda, T., A. Amemiya, S. Otsuka, J. Taylor, Y. Maejima, S. Nishizawa, T. Yamaura, K. Sueki, H. Tomita, and T. Miyoshi, 2022: Advantage of 30-s-Updating Numerical Weather Prediction with a Phased-Array Weather Radar over Operational Nowcast for a Convective Precipitation System, Geophys. Res. Lett., 49(11), e2021GL096927. doi:10.1029/2021GL096927
  12. Yeh, H.-L., S.-C. Yang, K. Terasaki, T. Miyoshi, and Y.-C. Liou, 2022: Including observation error correlation for ensemble radar radial wind assimilation and its impact on heavy rainfall prediction, Q. J. R. Meteorol. Soc. , 148(746), 2254-2281. doi.org/10.1002/qj.4302
  13. Sun, C., S. Richard, T. Miyoshi, N. Tsuzu, 2022: Analysis of COVID-19 spread in Tokyo through an agent-based model with data assimilation. J. Clin. Med. , 11(9), 2401. doi.org/10.3390/jcm11092401
  14. Terasaki, K., and T. Miyoshi, 2022: A 1024-Member NICAM-LETKF Experiment for the July 2020 Heavy Rainfall Event. SOLA, 18A, 8-14. doi.org/10.2151/sola.18A-002
  15. Miyoshi, T. and Q. Sun, 2022: Control simulation experiment with the Lorenz's butterfly attractor, Nonlin. Processes Geophys., 29, 133-139. https://doi.org/10.5194/npg-29-133-2022
  16. Maejima, Y., T. Kawabata, H. Seko and T. Miyoshi, 2022: Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event. SOLA, 2022, Volume 18, Pages 25-32. https://doi.org/10.2151/sola.2022-005
  17. Terasaki,K. and T. Miyoshi, 2022: Ensemble Kalman Filter Experiments at 112-km and 28-km Resolution for the Record-Breaking Rainfall Event in Japan in July 2018. In: Park S.K., Xu L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV). Springer, Cham. 525-542. https://doi.org/10.1007/978-3-030-77722-7_20
  18. Miyoshi, T., K. Terasaki, S. Kotsuki, S. Otsuka, Y.-W. Chen, K. Kanemaru, K. Okamoto, K. Kondo, G.-Y. Lien, H. Yashiro, H. Tomita, M. Sato, and E. Kalnay, 2022: Enhancing data assimilation of GPM observations. In: Silas M. (Eds) Precipitation Science, Measurement Remote Sensing, Microphysics, and Modeling. Elsevier, 787-804. doi:10.1016/B978-0-12-822973-6.00020-2
  19. Kotsuki, S., and Bishop, H. C. (2022): Implementing Hybrid Background Error Covariance into the LETKF with Attenuation-based Localization: Experiments with a Simplified AGCM. Mon. Wea. Rev., 150, 283-302. doi: 10.1175/MWR-D-21-0174.1

Invited Presentations

  1. Takemasa Miyoshi, Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations, PMM session of the joint PI meeting of JAXA Earth Observation Missions FY2021, Online, January 12, 2022.
  2. Kotsuki, S., and Bishop, H. C.: Implementing Hybrid Background Error Covariance into the LETKF with Attenuation-based Localization: Experiments with a Simplified AGCM. Data-Assimilation Workshop, Online. 17th February 2022.
  3. Taylor. J., Amemiya, A., Honda, T., Otsuka, S., Miyoshi, T., Convective-Scale Imbalance Induced by 30-Second Update Radar Data Assimilation, JpGU2022, Chiba, May 23, 2022
  4. Takemasa Miyoshi, Data assimilation research using Fugaku at RIKEN, Meteorology Colloquium, LMU, Munich, Germany, May 31, 2022
  5. Taylor. J., Amemiya, A., Honda, T., Otsuka, S., Miyoshi, T., Convective-Scale Imbalance Induced by 30-Second Update Radar Data Assimilation, JpGU2022, Chiba, May 23, 2022
  6. Taylor. J., Amemiya, A., Honda, T., Otsuka, S., Maejima, Y., Miyoshi, T., Convective-Scale Imbalance Induced by 30-Second Update Radar Data Assimilation, ISDA2022, Fort Collings, CO, USA, June 6, 2022
  7. Takemasa Miyoshi, A. Amemiya, T., Honda, T., Otsuka, S., Maejima, Y., Taylor, J., Tomita, H., Nishizawa, S., Sueki, K.Yamaura, T., Ishikawa, Y., Satoh, S., Ushio, T., Koike, K., Hoshi, E., Big data assimilation: Real-time 30-s-update forecast experiments using Fugaku in Tokyo in 2021, ISDA2022, Fort Collings, CO, USA, June 9, 2022, Keynote
  8. Taylor. J., Amemiya, A., Honda, T., Otsuka, S., Miyoshi, T., Sensitivity Testing with Localization Scale for a ConvectiveS-cale Ensemble Radar Data Assimilation System with 30-Sec Update, ISDA 2022, Fort Collings, CO, USA, June 9, 2022
  9. Takemasa Miyoshi, Fusing Big Data and Big Computation in Numerical Weather Prediction, International HPC Summer School 2022, Athens, Greece, June 20, 2022, Keynote
  10. Takemasa MIYOSHI, Qiwen SUN, Controllability of Extreme Events with the Lorenz-63 Model, Science Society in Clubhouse, online, July 15, 2022
  11. Takemasa Miyoshi, Sun, Q., Terasaki, K. and Maejima, Y.: From Predictability to Controllability: Control Simulation Experiment, AOGS2022, 19th Annual Meeting, Online, August 4, 2022
  12. Takemasa Miyoshi, Big Data Assimilation: Real-time 30-s-update Forecast Experiments Using Fugaku in Tokyo in 2021, AOGS2022, 19th Annual Meeting, Online, August 5, 2022
  13. Takemasa Miyoshi, Big Data Assimilation Real-Time 30-s-update ExperimentsUsing Fugakuin Tokyo in 2021, WWRP Symposium, online, August 24, 2022
  14. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, 2nd US-Japan Workshop on Data-Driven Fluid Dynamics, Kobe, September 6, 2022, Keynote
  15. Kotsuki, S., Ouyang, M., Saito, T. and Shiojiri, D., Combining Data Assimilation and Sparse Sensing Placement Method For Designing Better Observing Networks, RIKEN Data Assimilation Seminar, Online, September 14, 2022
  16. Konduru Rakesh Teja, Masato I. Nodzu, and Jun Matsumoto, Satellite Observed seasonal and annual variation of Yakushima Island precipitation, Association of Japanese Geographers Autumn meeting 2022 , Takamatsu, September 23, 2022
  17. Takemasa Miyoshi, Big Data Assimilation: Real-time 30-s-update Torrential Rain Forecast Using Fugaku in Tokyo in 2021, The 5th ISEE Symposium Toward the Future of Space-Earth Environmental Research, Nagoya, November 15, 2022
  18. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, University of Reading Data Assimilation Research Center and RIKEN Online Joint Seminar Series, Reading, UK, November 23, 2022
  19. Takemasa Miyoshi, Big Data Assimilation revolutionizing numerical weather prediction using Fugaku, Statistical Science Seminar, University of College London, London, UK, November 24, 2022
  20. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, AOSC Seminar/University of Maryland, College Park, Maryland, USA, December 1, 2022

Honors and Awards

  1. Miyoshi Takemasa: 'Awards for Science and Technology' The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology, "数値天気予報を革新するビッグデータ同化の研究", 8th April 2022.
  2. Miyoshi Takemasa,Kouji Terasaki: RIKEN BAIHO Award, Construction of new weather forecast system by large-scale ensemble computations using global cloud resolving model and localized ensemble Kalman filter on Fugaku., 23rd March 2022.
  3. James Taylor: RIKEN Oubu Research Incentive Award, Oversampling Reflectivity Observations from a Geostationary Precipitation Radar Satellite: Impact on Typhoon Forecasts within a Perfect Model OSSE Framework. 23rd March 2022.

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