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.
Research Areas
Natural sciences / Atmospheric and hydrospheric science / Data Assimilation, Weather Forecast, Hydrological Modeling, Climate Change
Social infrastructure (civil Engineering, architecture, disaster prevention) / Hydroengineering /
Informatics / Computational science / Data Assimilation, Deep Learning, Mathematical Modeling
Shunji Kotsuki Takemasa Miyoshi Keiichi Kondo Roland Potthast
Geoscientific Model Development 15 8325-8348 Nov 2022 [Refereed]
Abstract. A particle filter (PF) is an ensemble data assimilation method that does not assume Gaussian error distributions. Recent studies proposed local PFs (LPFs), which use localization as in the ensemble Kalman filter, to apply the PF for high...
The Proceedings of Mechanical Engineering Congress, Japan 2021 J051-02 2021
There is an increasing demand for a technological infrastructure that can efficiently design, predict, and control products throughout the product life cycle. In the case of digital twin technology and cyber-physical systems, there is a gap betwee...
環北極域における超高頻度衛星観測データの創出による陸面劇的変動の早期高精度検出Japan Society for the Promotion of Science: Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory)
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