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研究室情報: 千葉大学・CEReS・小槻研究室のHPへは こちらへアクセスしてください。(2021/04/01)


■BRIEF INTRODUCTION :: Shunji Kotsuki, Ph. D. (小槻 峻司)
(1) Associate Professor, Center for Environmental Remote Sensing (CEReS), Chiba University, Japan
(2) PRESTO Researcher (SAKIGAKE), Japan Science and Technology Agency, Japan
(3) Visiting Scientist, RIKEN Center for Computational Science (R-CCS), Japan

(1) 千葉大学 環境リモートセンシング研究センター 准教授
(2) 科学技術振興機構 さきがけ 研究員
(3) 理化学研究所 計算科学研究センター 客員研究員

■BRIEF NARRATIVE SUMMARY

Dr. Shunji Kotsuki is an Associate Professor of Center for Environmental Remote Sensing (CEReS), Chiba University, and leading "Environmental Prediction Science". He received his B.S. (2009), M.S.(2011) and Ph. D. (2013) degrees in civil engineering from Kyoto University. He experienced his professional career as Post-doctoral Researcher (2014-2017), and Research Scientist (2017-2019) at RIKEN Center for Computational Science (R-CCS). He started leading his research group at CEReS, Chiba University since November, 2019.

Dr. Kotsuki is a leading scientist on data assimilation and numerical weather prediction with over 6 years of research experience in development of the global atmospheric data assimilation system (a.k.a. NICAM-LETKF). His research interests are in data assimilation mathematics, model parameter estimation, observation diagnosis including impact estimates, satellite data analysis, hydrological modeling, and atmospheric and hydrological disaster predictions. His techniques for an adaptive covariance inflation and assimilating observations with non-Gaussian errors have been incorporated in the RIKEN’s global atmospheric data assimilation system, and improved its weather forecasts significantly.

In 2017, Dr. Kotsuki was selected as an Excellent Young Researcher by Ministry of Education, Culture, Sports, Science and Technology, Japan. He has been recognized by several prestigious awards such as the Thesis Award for Young Scientists from Japan Society of Hydrology and Water Resources Engineering (2013), and RIKEN Ohbu Research Incentive Award (2019). He is also the PRESTO researcher of JST, and visiting scientist of R-CCS, and exploring data-driven approaches for the environmental prediction science.

Please see [Full Version of CV (PDF file)] for more detail.


■ TOP 10 SELECTED PUBLICATIONS [Link to All Publication List]

  1. Kotsuki S., Sato Y., and Miyoshi T. (2020): Data Assimilation for Climate Research: Model Parameter Estimation of Large Scale Condensation Scheme. J. Geophys. Res., 125, e2019JD031304. doi: 10.1029/2019JD031304 [JGR-A]
  2. Kotsuki S., Kurosawa K., Otsuka S., Terasaki K. and Miyoshi T. (2019): Global Precipitation Forecasts by Merging Extrapolation-based Nowcast and Numerical Weather Prediction with Locally-optimized Weights. Wea. and Forecasting, 34, 701-714. doi:10.1175/WAF-D-18-0164.1 [WAF]
  3. Kotsuki S., Kurosawa K., and Miyoshi T. (2019): On the Properties of Ensemble Forecast Sensitivity to Observations. Q. J. R. Meteorol. Soc., 145, 1897-1914. doi: 10.1002/qj.3534 [QJRMS]
  4. Kotsuki S., Terasaki K., Kanemaru K., Satoh M., Kubota T. and Miyoshi T. (2019): Predictability of Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Near-real-time Global Atmospheric Data Assimilation System NEXRA. SOLA, 15A, 1-7. doi: 10.2151/sola.15A-001 [SOLA]
  5. Kotsuki S., Terasaki K., Yashiro H., Tomita H., Satoh M. and Miyoshi T. (2018): Online Model Parameter Estimation with Ensemble Data Assimilation in the Real Global Atmosphere: A Case with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data. Journal of Geophysical Research: Atmospheres, 123, 7375-7392. doi: 10.1029/2017JD028092 [JGR-A]
  6. Kotsuki S., Greybush S., and Miyoshi T. (2017): Can we optimize the assimilation order in the serial ensemble Kalman filter? A study with the Lorenz-96 model. Monthly Weather Review, 145, 4977-4995. doi: 10.1175/MWR-D-17-0094.1 [MWR]
  7. Kotsuki S., Ota Y., and Miyoshi T. (2017): Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quarterly Journal of the Royal Meteorological Society, 143, 2001-2015. doi: 10.1002/qj.3060 [QJRMS]
  8. Kotsuki S., Miyoshi T., Terasaki K., Lien G.Y. and Kalnay E. (2017): Assimilating the Global Satellite Mapping of Precipitation Data with the Nonhydrostatic Icosahedral Atmospheric Model NICAM. Journal of Geophysical Research: Atmospheres, 122, 631-650. doi:10.1002/2016JD025355 [JGR-A]
  9. Kotsuki S. and Tanaka K. (2015): SACRA - a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI. Hydrology and Earth System Sciences, 19, 4441-4461. doi: 10.5194/hess-19-4441-2015 [HESS] [Discussion] [OpenDATA]
  10. Kotsuki S., Terasaki K., and Miyoshi T. (2014): GPM/DPR Precipitation Compared with a 3.5-km-resolution NICAM Simulation. SOLA, 10, 204-209. doi:10.2151/sola.2014-043 [SOLA]


NOTES


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