Achievements in 2023

Peer-reviewed papers

  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. Terasaki, K., and T. Miyoshi, 2023: Including the horizontal observation error correlation in the ensemble Kalman filter: idealized experiments with NICAM-LETKF. Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-23-0053.1
  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

Invited Presentations

  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. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. Takemasa Miyoshi, Chaos implies effective controllability of extreme weather, The Third International Nonlinear Dynamics Conference (NODYCON 2023), Rome, Italy, June 19, 2023, Keynote
  9. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, The 28th IUGG General Assembly (IUGG2023), Berlin, July 14, 2023
  10. 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
  11. 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
  12. 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
  13. 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

Honors and Awards

  1. Yasumitsu Maejima, Takuya Kawabata, Hiromu Seko, and Takemasa Miyoshi: MSJ SOLA Awards: Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event. February 2023.
  2. Shun Ohishi: RIKEN Oubu Research Incentive Award, Development of an ensemble Kalman filter-based ocean data assimilation system. 22nd March 2023.
  3. Iyan Mulia: RIKEN Oubu Research Incentive Award, Machine learning-based tsunami inundation prediction. 22nd March 2023.
  4. Takemasa Miyoshi:RIKEN EIHO Award, Pioneering Meteorological research on big data assimilation and controllability. 22nd March 2023.

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