Achievements in 2024

Peer-reviewed papers

  1. Terasaki, K. and T. Miyoshi, 2024: Including the horizontal observation error correlation in the ensemble Kalman filter: idealized experiments with NICAM-LETKF. Mon. Wea. Rev., 152, 277-293. doi:10.1175/MWR-D-23-0053.1
  2. Liang, J., N. Sugimoto, and T. Miyoshi, 2024: Analyzing the Instabilities in the Venus Atmosphere Using Bred Vectors. Journal of Geophysical Research: Planets, 129, e2023JE008067. doi:10.1029/10.1029/2023JE008067
  3. Ohishi, S., T. Miyoshi, and M. Kachi, 2024: Impact of atmospheric forcing on SST in the LETKF-based ocean research analysis (LORA), Ocean Modelling, 189, 102357. doi:10.1016/j.ocemod.2024.102357
  4. Takeda, K., S. Kaji, and T. Miyoshi, 2024: Topological regularization on numerical simulations of the advection equation, JSIAM Letters, 16, 53-56. doi:10.14495/jsiaml.16.53
  5. Furukawa, K., H. Sakamoto, M. Ohhigashi, S. Shima, T. Sluka, and T. Miyoshi, 2024: Particle filter data assimilation for ubiquitous unstable trajectories of two-dimensional three-state cellular automata. Nonlinear Dyn., doi:10.1007/s11071-024-09803-5
  6. Ohishi, S., T. Miyoshi, T. Ando, T. Higashiuwatoko, E. Yoshizawa, H. Murakami, and M. Kachi, 2024: LETKF-based Ocean Research Analysis (LORA) version 1.0, Geoscience Data Journal, 11, 995-1006. doi:10.1002/gdj3.271
  7. Takeda, K., and T. Sakajo, 2024: Uniform error bounds of the ensemble transform Kalman filter for chaotic dynamics with multiplicative covariance inflation, SIAM/ASA Journal on Uncertainty Quantification, 12(4), 1315-1335. doi:10.1137/24M1637192
  8. Li, L., J. Li, and T. Miyoshi, 2024: Chaos suppression through Chaos enhancement, Nonlinear Dyn., doi:10.1007/s11071-024-10426-z
  9. Muto, Y. and S. Kotsuki, 2024: Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation, Hydrol. Earth Syst. Sci., 28, 24, 5401-5417. doi:10.5194/hess-28-5401-2024

Invited Presentations

  1. K. Furukawa, Well-Posedness of One-Dimensional Drift-Diffusion Equations under Dynamic Conditions and the Fourth Boundary Condition, The 25th Northeastern Symposium on Mathematical Analysis, Hokkaido University, Sapporo, February 20, 2024
  2. Kota Takeda, Takashi Sakajo: Theoretical aspect of the ETKF and covariance inflation, DA Seminary at IMT Atlantique, IMT Atlantique, March 14, 2024
  3. Takemasa Miyoshi, Advances and applications of satellite data assimilation of clouds, precipitation, and the ocean, DA Forum by University of Melbourne, Bureau of Meteorology, Melbourne, Australia, March 15, 2024
  4. Takemasa Miyoshi, Tokyo Olympics/Paralympics forecast experiment with phased array weather radar, Deepdive session at IMT Atlantique, Brest, France, April 3, 2024
  5. Kota Takeda, Takashi Sakajo: Uniform error bounds of the ensemble transform Kalman filter with multiplicative covariance inflation for chaotic dynamics, 25th Prediction Science Seminar, R-CCS, April 12, 2024
  6. Kota Takeda, Takashi Sakajo: Uniform error bounds of the ensemble transform Kalman filter for infinite-dimensional dynamics with multiplicative covariance inflation, Tohoku Univ. AIMR, April 19, 2024
  7. Kota Takeda: The Essence of Data Assimilation, The 1st SIAM Student Chapter Kyoto Seminar, Kyoto University, May 17, 2024
  8. Konduru, R. T.: Unravelling the Urbanization Effects on the Extreme Rainfall Evnets: Insights from Mesoscale to Large Eddy Model simulations. The regional climate system laboratory of the Manila Observatory lecture series, Manila, Phillipine, May 23, 2024
  9. Takemasa Miyoshi, Every 30-second Phased Array Radar Data Assimilation Proven Effective for Short-range Convective Weather Forecast, 8th WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction and Earth System Prediction, Norrköping, Sweden, May 28, 2024, Keynote
  10. Takemasa Miyoshi, Big Data Assimilation: Real-time 30-Second-Refresh Heavy Rain Forecast Using Fugaku during Tokyo Olympics and Paralympics, Central Weather Administration, Chinese Taipei, June 18, 2024
  11. Takemasa Miyoshi, Toward next 100 years of data assimilation and numerical weather prediction, MSROC Centennial Celebration and Symposium, Central Weather Administration, Chinese Taipei, June 19, 2024, Keynote
  12. Takemasa Miyoshi, Big Data Assimilation Revolutionizing Numerical Weather Prediction Using Fugaku, 24th International Conference on Computational Science (ICCS2024), Malaga, Spain, July 2, 2024, Keynote
  13. Takemasa Miyoshi: Toward next 100 years of data assimilation and numerical weather prediction?The CRC International Summer school 2024, Boltenhagen, September 17, 2024
  14. Takemasa Miyoshi: Toward efficient control of extreme weather events?The CRC International Summer school 2024, Boltenhagen, September 17, 2024
  15. Kota Takeda, Takashi Sakajo: Mathematical analysis of data assimilation and related topics, Applied Mathematics Freshman Seminar 2024, Kyoto University, November 3, 2024
  16. Kota Takeda, Takashi Sakajo: Uniform error bounds of the ensemble square root filter for chaotic dynamics with multiplicative covariance inflation, The 1st MMS Workshop for Young Researchers, Kyoto University, November 20, 2024
  17. Konduru, R. T., Harnessing Satellite Data and Large Eddy Simulations to Unveil Extreme Rainfall over Urban Skylines. Science Frontier Geoscience Seminar, Osaka Metropolitan University, Osaka, November 26, 2024

Honors and Awards

  1. Yasumitsu Maejima: RIKEN Oubu Research Incentive Award, "Observing System Simulation Experiments of a Rich Phased Array Weather Radar Network Covering Kyushu for the July 2020 Heavy Rainfall Event", 12th March 2024.
  2. Konduru Rakesh Teja: IPWG-11 Best Poster Awards (Highly Commended Prize), "Improving Small-scale Tropical Precipitation Forecast by Assimilating Frequent Satellite Microwave Observations", 18th July 2024.
  3. Konduru Rakesh Teja: ISDA2024 Poster Award, "Improving Small-scale Tropical Precipitation Forecast by Assimilating Frequent and Dense Satellite Microwave Observations", 25th October 2024.

Working with us

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Data Assimilation Research Team

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RIKEN Center for Computational Science (R-CCS) Data Assimilation Research Team

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