last update: 2026.1.26

Peer-reviewed articles

  1. Guerrieri, J. M., Pulido, M. A., Miyoshi, T., Amemiya, A., & Ruiz, J. J. (2026). Localization in the mapping particle filter. EGUsphere, 2026, 33(1), 33-49, https://doi.org/10.5194/npg-33-33-2026
  2. Amemiya, A., & Miyoshi, T. (2026). Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30-second radar observation with ensemble Kalman filter: idealized experiments of deep convection. Nonlinear Processes in Geophysics, 2026, 33(1), 1-16, https://doi.org/10.5194/npg-33-1-2026
  3. Huo, Z., Y. Liu, J. Taylor, Y. Zhou, A. Amemiya, H. Fan, T. Miyoshi (2025): Incremental Analysis Updates in a Convective-Scale Ensemble Kalman Filter Using Minute-by-Minute Phased Array Radar Observations Journal of Advances in Modeling Earth Systems, 17,e2024MS004802.https://doi.org/10.1029/2024MS004802
  4. 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. https://doi.org/10.1007/978-3-031-40567-9_18
  5. Miyoshi, T., Amemiya, A., Otsuka, S., Maejima, Y., Taylor, J., Honda, T., Tomita, H., Nishizawa, S., Sueki, K., Yamaura, T., Ishikawa, Y., Satoh, S., Ushio, T., Koike, K., and Uno, A. (2023). Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 8, 1–10.https://doi.org/10.1145/3581784.3627047
  6. Taylor, J., Honda, T., Amemiya, A., Maejima, Y., and Miyoshi, T. (2023). Sensitivity to Localization Radii for an Ensemble Filter Numerical Weather Prediction System with 30-Second Update, Weather and Forecasting,https://doi.org/10.1175/WAF-D-21-0177.1
  7. Amemiya, A., Mohta, S., and Miyoshi, T. (2023). Application of recurrent neural networks to model bias correction: Idealized experiments with the Lorenz-96 model, Journal of Advances in Modeling Earth Systemss, 15, e2022MS003164. https://doi.org/10.1029/2022MS003164
  8. Honda, T., Amemiya, A., Otsuka, S., Taylor, J., Maejima, Y., Nishizawa, S., et al. (2022). Advantage of 30-s-updating numerical weather prediction with a phased-array weather radar over operational nowcast for a convective precipitation system. Geophysical Research Letters, 49, e2021GL096927. https://doi.org/10.1029/2021GL096927
  9. Honda, T., Amemiya, A., Otsuka, S., Lien, G.-Y., Taylor, J., Maejima, Y., et al. (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. Journal of Advances in Modeling Earth Systems, 14, e2021MS002823.https://doi.org/10.1029/2021MS002823
  10. Taylor, J., Amemiya, A., Honda, T., Maejima, Y., & Miyoshi, T. (2021). Predictability of the July 2020 Heavy Rainfall with the SCALE-LETKF. Sola, 17(July 2020), 48–56.,
    doi:https://doi.org/10.2151/sola.2021-008
  11. Amemiya, A. and Sato, K., Characterizing quasi-biweekly variability of the Asian monsoon anticyclone using potential vorticity and large-scale geopotential height field, Atmospheric Chemistry and Physics, 20.22 (2020),
    doi:https://doi.org/10.5194/acp-2020-424
  12. Amemiya, A., T. Honda and T. Miyoshi, Improving the observation operator for the Phased Array Weather Radar in the SCALE-LETKF system, SOLA, 16 (2020), 6-11,
    doi:http://dx.doi.org/10.2151/sola.2020-002
  13. Amemiya, A., and K. Sato, A two dimensional dynamical model for the subseasonal variability of the Asian monsoon anticyclone, Journal of the Atmospheric Sciences, 75.10 (2018): 3597-3612:
    doi: http://dx.doi.org/10.1175/JAS-D-17-0208.1
    Summary pdf
  14. Amemiya, A., and K. Sato, A new gravity wave parameterization including three dimensional propagation, Journal of the Meteorological Society of Japan, Ser. II 94.3 (2016): 237-256.
    doi: http://dx.doi.org/10.2151/jmsj.2016-013
    Summary pdf

Presentation

After 2018.11

  1. Arata Amemiya, Shlok Mohta and Takemasa Miyoshi (Poster) ,
    Application of the Long-Short Term Memory neural networks to model bias correction: idealized experiments with the Lorenz-96 model, Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, October 5 - October 8 (October 5), 2020 .

Before 2018.10

  1. Arata Amemiya and Kaoru Sato (Poster) ,
    Characterizing the quasi-biweekly variability of the anticyclone in the upper troposphere and lower stratosphere over the Asian monsoon region, SPARC General Assembly 2018, Miyako Messe, Kyoto, October 1 - October 5 (October 3-5), 2018 .
  2. Arata Amemiya and Kaoru Sato (Oral) ,
    A simple two dimensional model for the subseasonal variability of the asian monsoon anticyclone, SPARC Joint Workshop, Kyoto University, Kyoto, October 9 - October 13 (October 11), 2017 .
  3. Arata Amemiya and Kaoru Sato (Oral) ,
    A study on gravity wave parameterization including three-dimensional propagation,
    AOGS the 12th Annual Meeting, Singapore, Singapore, August 3 - August 7 (August 6), 2015.
  4. Arata Amemiya and Kaoru Sato (Oral) ,
    Possibility of inertial instability around the Asian summer monsoon,
    The 2nd workshop on atmospheric composition and the Asian summer monsoon(ACAM), Bangkok, Thailand, June 8 - 10 (June 10), 2015 .
  5. Arata Amemiya and Kaoru Sato (Poster) ,
    A New Gravity Wave Drag Parameterization Scheme Including Three Dimensional Propagation,
    AOGS the 11th Annual Meeting, Sapporo, Japan, July 28 - August 1 (July 30), 2014.