Achievements in 2020

Peer-reviewed papers

  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.
  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.
  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.
  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.
  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., Quart. J. Roy. Meteor. 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.
  10. Necker, T., S. Geiss, M. Weissmann, J. Ruiz, T. Miyoshi, G.-Y. Lien, 2020: A convective-scale 1000-member ensemble simulation and potential applications. Quart. J. Roy. Meteor. Soc., 146, 1423-1442. doi:10.1002/qj.3744
  11. Necker, T., M. Weissmann, Y. Ruckstuhl, J. Anderson, and T. Miyoshi, 2020: Sampling error correction evaluated using a convective-scale 1000-member ensemble. Mon. Wea. Rev., 148, 1229-1249. doi:10.1175/MWR-D-19-0154.1
  12. Maejima, Y. and T. Miyoshi, 2020: Impact of the window length of four-dimensional local ensemble transform Kalman filter: a case of convective rain event. SOLA, 16, 37-42. doi:10.2151/sola.2020-007
  13. Amemiya, A., T. Honda, and T. Miyoshi, 2020: Improving the observation operator for the Phased Array Weather Radar in the SCALE-LETKF system. SOLA, 16, 6-11. doi:10.2151/sola.2020-002
  14. Kotsuki S., Y. Sato, and T. Miyoshi, 2020: Data Assimilation for Climate Research: Model Parameter Estimation of Large Scale Condensation Scheme. J. Geophys. Res., 125, e2019JD031304. doi:10.1029/2019JD031304

Invited Presentations

  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. 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.

Honors and Awards

  1. Ohishi, S.: JOC The Young Author Award: Frontolysis by surface heat flux in the eastern Japan Sea: importance of mixed layer depth 28th November 2020.
  2. Miyoshi, T.: FY2020 Prime Minister's Commendations to Contributors for Disaster Prevention. 1st September 2020.
  3. Shigenori Otsuka: RIKEN Oubu Research Incentive Award, Development of a novel three-dimensional precipitation nowcast method and its real-time demonstration. 3rd April 2020.

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