Shunji Kotsuki, Ph. D. (小槻 峻司)
Professor
Institute for Advanced Academic Research (IAAR), Chiba University
Address: Chiba-U., Yayoi-Cho 1-33, Inage-Ku, Chiba 263-8522, Japan
E-mail:shunji.kotsuki@chiba-u.jp
Website:http://www.kotsuki-shunji.com
Laboratory: https://kotsuki-lab.com/

Last update: March 7, 2024

Professional Experience (Main Affiliations)2022‐07–Present

Professor
Institute for Advanced Academic Research (IAAR)), Chiba University, Japan, Chiba
University (concurrent; Center for Environmental Remote Sensing (CEReS) since2022/06), Research Institute of Disaster Medicine (RIDM) since 202305, andDepartment of Information Engineering, Faculty of Engineering; since 2020/01)

2019/11–2022/06 Associate Professor
Center for Environmental Remote Sensing (CEReS), Chiba University, Japan, Chiba
University
2017/10–2019/10 Research Scientist
Data Assimilation Research Team, RIKEN Center for Computational Science (RCCS), Kobe, Japan (concurrent; RIKEN interdisciplinary Theoretical andMathematical Sciences Program 2018/04-2019/10, Cluster for Pioneering
Research 2019/04-2019/10),
2014/01–2017/10 Postdoctoral Researcher
Data Assimilation Research Team, RIKEN Advanced Institute for Computational
Science (AICS), Kobe, Japan
2013/12–2013/12 Postdoctoral Researcher
Japan Society for the Promotion of Science, Japan
2012/04–2013/11 Research Fellow
Japan Society for the Promotion of Science, Japan

Professional Experience (Concurrent Affiliations)
2023/09–Present Visiting Scientist
Prediction Science Laboratory, RIKEN Cluster for Pioneering Research (CPR), Kobe,Japan
2019/11–Present Visiting Scientist
Data Assimilation Research Team, RIKEN Center for Computational Science (RCCS), Kobe, Japan1

2019/10–2023/03 Researcher
PRESTO, Japan Science and Technology Agency, Kobe, Japan
2018/05–2019/10 Affiliate Associate Professor
Graduate School of Science, Kyoto University, Japan
2017/10–2019/10 Excellent Young Researcher
Initiative for Excellent Young Researchers, Ministry of Education, Culture, Sports,
Science and Technology, Japan
2017/04–2019/10 Part‐time Lecturer
Graduate School of Science, Kyoto University, Japan
2011/04–2012/03 Research Assistant
Disaster Prevention Research Institute, Kyoto University, Japan

Education
2013/11 Ph. D.

Engineering (Urban Management Engineering)
Graduate School of Engineering, Kyoto University, Japan

2011/03 M. S.

Engineering (Urban Management Engineering)
Graduate School of Engineering, Kyoto University, Japan

2009/03 B. S.

Engineering (Civil Engineering)
Faculty of Engineering, Kyoto University, Japan

Awards to Research Achievements
2022/04

Young Scientist Award
Ministry of Education, Culture, Sports, Science, and Technology, Japan"Environmental Prediction Studies using Satellite Big Data and Supercomputers”

2021/11

Chiba University Award for Distinguished Researcher"Environmental and Disaster Predictions Through Numerical Simulation and Data
Assimilation with High Performance Computers"

2019/03

RIKEN Ohbu Award
RIKEN Incentive Research Award, Japan"Advancing Ensemble Data Assimilation-based Global Weather Forecast System"

2017/10

Leading Initiative for Excellent Young Researchers (LEADER)
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan"Advancing Real-Time Weather and Hydrological Predictions with Data
Assimilation and Artificial Intelligence"

2013/09

Thesis Award for Young Scientists
Japan Society of Hydrology and Water Resources2

2013/02

HUME Prize (Top three master dissertation of the department)
Department of Urban Management, Graduate School of Engineering, Kyoto
University, Japan

Awards to Presentations
2021/09

Outstanding Presentation Award
JSHWR-JAHS Joint Symposium in 2021

2021/06

Outstanding Discussion Award
65th Conference of Japan Society of Civil Engineers, Japan

2020/11

Outstanding Presentation Award
5th Global Prominent Symposium of Chiba University, Japan

2020/06

Outstanding Discussion Award
64th Conference of Japan Society of Civil Engineers, Japan

2013/08

Outstanding Presentation Award
6th Conference of the Asia Pacific Association of Hydrology and Water Resources

2013/05

Best Presentation Award
Remote Sensing Society of Japan

2013/02

Best Presentation Award
Annual Conference, Disaster Prevention Research Institute, Kyoto University, Japan

Funded Projects as the lead PI
2023/12–2027/03 MOONSHOT Research & Development Program (Core), JST, Japan
Artificial generation of upstream maritime heavy rains to govern intense-raininduced disasters over land (about 1,200,000K JPY)
2023/06–2025/03 Collaborative Research, RTRI (Railway Technical Research Institute), Japan
Development of Evaluation Methods for the Spatial Representativeness of Ground
Weather Observations (4,000K JPY)
2023/04–2023/12 FOREST (Fusion Oriented Research for Disruptive Science and Technology), JST, Japan
Foreseeing Unprecedented Flood Disasters through Integration of Simulation
Science and Folklore (65,000K JPY)
2022/06–2025/03 Grant‐in‐Aid for Challenging Research (Exploratory), JSPS, Japan
Exploring Cost-effective Observation Placements Using The Data-driven Sparse
Sensor Placement (6,500 K JPY)
2022/06–2024/03 Grants‐in‐Aid for Transformative Research Areas Foundation (A) (Publicly
Offered Research), JSPS, Japan
Investigating Flood Risk Impacts on Local Community Developments throughNumerical Simulations and Area Studies (10,400K JPY)

3

2022/04–2028/03 Advanced Academic Research Support Program (top leader), Chiba
University, Japan
Exploring Frontiers in Global Environment and Disaster Predictions ThroughIntegration of Big Satellite Observations and Data Science (about 80,000K JPY)
2022/04–2025/03 JAXA Third Research Announcement (RA3), JAXA, Japan
Advancing GSMaP Precipitation by Land Data Assimilation and Data Science (about
13,000K JPY)
2022/04–2023/11 MOONSHOT Research & Development Program (feasibility), JST, Japan
Quantifying Weather Controllability and Mitigatable Flood Damage Based on
Ensemble Weather Forecast (about 80,000K JPY)
2021/04–2025/03 Grants‐in‐Aid for Scientific Research Foundation (A), JSPS, Japan
Exploring Real-time Rainfall and Flood Predictions in Fugaku Era with the State-ofthe-art Data Science (41,860K JPY)
2019/10–2023/03 PRESTO (Precursory Research for Embryonic Science and Technology), JST, Japan
Advancing Data Assimilation and Prediction Methods to Maximize "The Value of
Observations" (82,160K JPY)
2018/04–2020/03 Grants‐in‐Aid for Scientific Research Foundation (B), JSPS, Japan
Land-Atmosphere-Coupled Data Assimilation: Improving Atmospheric andHydrological Predictions by Hydrological Big Data Assimilation (17,420K JPY)
2017/10–2019/03 Leading Initiative for Excellent Young Researchers, MEXT, Japan
Advancing Real-Time Weather and Hydrological Predictions with Data
Assimilation and Artificial Intelligence (18,000K JPY)
2015/04–2018/03 Grant‐in‐Aid for Young Scientists (B), JSPS, Japan
Process-based Crop Yield Prediction Using Satellite Observations (4,160K JPY)
2012/04–2013/12 Grant‐in‐Aid for Fellows, JSPS, Japan
Estimating Global Crop Yield Potential Using a Global Agricultural Water Resources
Model (1,800K JPY)

Funded Projects as the Co‐PI (& Funds as Co‐PI)
2022/06–2025/03 Grants‐in‐Aid for Challenging Research (Exploratory), JSPS, Japan, PI: Prof. K.
Ichii (U. Chiba)
English Title TBD (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)2021/07–2026/03 Grants‐in‐Aid for Scientific Research Foundation (S), JSPS, Japan, PI: Prof. T.
Oki (U. Tokyo)
Study on global terrestrial hydrodynamics with satellite earth observations(20,930K JPY)

4

2021/04–2024/03 Environment Research and Technology Development Fund of the Ministry of
the Environment, Japan [Strategic Research] PI: Prof. K. Ichii (Chiba U.)
English Title TBD (観測データ及びモデル推定の統合解析による陸域の GHG 収
支評価; 15,000K JPY)
2021/04–2024/03 Environment Research and Technology Development Fund of the Ministry of
the Environment, Japan [Young Research] PI: Prof. Y. Igarashi (Fukushima U.)
Prediction System for Radionuclides Redistribution due to Wild Fire inContaminated Regions (10,946K JPY)
2020/04–2022/03 河川砂防技術研究開発公募, PI: Prof. S. Watanabe (U. Tokyo)
English Title TBD (大規模気候データを活用したこれからの河川計画策定に向けた技術開発; 343.2K JPY)

Cooperative Research Projects
2023/04–2026/03 RIKE‐MOST Collaborative Research, PI: Prof. Miyoshi(RIKEN)
Comprehensive earthquake forecast model based on multi-geophysical data
assimilation
2022/04–2027/03 JSPS Core‐to‐Core Program, PI: Prof. Ichii (Chiba U.)
GEOLAND-NET (GEOstationary-satellite LAND monitoring NETwork)
2022/04–2027/03 Advanced studies of climate change projection, MEXT, Japan, PI: Prof. Mori
(Kyoto U.)
English Title TBD (ハザード統合予測モデルの開発)
2022/04–2025/03 JAXA Third Research Announcement, PI: Prof. Miyoshi (RIKEN)
Advances and applications of satellite data assimilation of clouds, precipitation,and the ocean
2021/07–2025/03 JST Mirai Program, PI: Prof. H. Tsukada (Chubu U.)
English Title TBD (非線形・複雑系に着目した認知症のロバストネス数理モデルとそのハブ因子の解明)
2020/07–2025/03 JICA‐JST SATREPS (Science and Technology Research Partnership for
Sustainable Development), PI: Prof. K. Tanaka (Kyoto U.)
Development of Innovative Climate Resilient Technologies for Monitoring andControlling of Water Use Efficiency and Impact of Salinization on Crop Productivity
and Livelihood in Aral Sea Region
2020/04–2023/03 Program for Promoting Researches on the Supercomputer Fugaku, MEXT, PI:
Prof. M. Satoh (U. Tokyo)
Large Ensemble Atmospheric and Environmental Prediction for Disaster
Prevention and Mitigation
2020/04–2023/03 Global Prominent Research of Chiba University, PI: Prof. N. Tsumura
Creation of Material Appearance and Affective Imaging

5

2020/04–2023/03 JAXA Precipitation Measurement Mission, PI: Prof. Miyoshi (RIKEN)
Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM
Observations
2013/04–2016/03 地球観測技術等調査研究委託事業, PI: Prof. A. Higuchi (Chiba U.)
食糧安全保障に向けた衛星入力を活用した環太平洋域での広域収量推定および短期予測の試み

Seeding Funds
2021/04–2022/03 ERAN Collaborative Research for Young Scientists, PI: Dr. J. Hu of Kotsuki Lab.

Quantify the fire trends and resuspension radionuclides using remotesensing data for radioactively contaminated forests in Ukraine
2020/04–2021/03 FY2020 ERAN Collaborative Research for Young Scientists
Implementing Radioactive Transport/Diffusion Modules into an Integrated
Hydrological Model for Long-term Projections

Press Release2021/07/07

Improving Typhoon Prediction with Geostationary Radar Observation
https://www.riken.jp/press/2021/20210707_1/index.html

2020/08/20

Global Precipitation Forecasting System by Simulation and Satellite Obs.
https://www.eorc.jaxa.jp/theme/NEXRA/index_e.htm

Certifications
2015

Certified and Accredited Meteorologists of Japan (ID: 9466)

2009

First‐class National Government Employee with specialty in Engineering
National Personnel Authority, the Government of Japan

Editorship of Scientific Journals
2020/06–Present Editor, Journal of the Meteorological Society of Japan
2020/09–Present Editor, Journal of the Japan Society of Hydrology and Water Resources

Committee Member of Scientific Activities
2023/11‐Present

Program Committee Members, Japan Geoscience Union Meeting 2024, JpGU

2022/12‐Present

Member of Hydrology Division, Committee on Hydroscience and HydraulicEngineering, JSCE

6

2022/09–Present Working Group Member of IHP‐IX Strategic Plan (Theme 3: Bridging the data
and knowledge gap)
2022/09–Present 水文・水資源学会

JpGU 対応委員会

委員

2022/07–Present JAXA・地球観測に関する科学アドバイザリ委員会・PMM 分科会（後継ミッショ
ン検討グループ & 利用促進検討グループ）

Thesis/Dissertation committee (completed)2023/03

Xiao Wang (The University of Tokyo; Prof. Kei Yoshimura)
Historical weather reconstruction by data assimilation of cloud cover from old
diaries

Scientific Organizing Committee
Data-driven approaches for weather and hydrological predictions, JpGU 2024 (main convener)
Hydrology & Water Environment Session, JpGU 2024 (main convener)
The RIKEN International School on Data Assimilation (RISDA 2018), Jan. 22-26, 2018.

Local Conference Organization (Co‐Chair)
The 7th International Symposium on Data Assimilation (ISDA2019), Jan. 21-24, 2019. (co-chair)
The RIKEN International School on Data Assimilation (RISDA 2018), Jan. 22-26, 2018. (co-chair)

Proposal Review
FY2018RIKEN Incentive Research Projects

Referee of Scientific Journals(01) Atmosphere (by MDPI)(01) Earth, Planets and Space (by Springer)(01) Earth and Space Sciences (by AGU)
(01) Hydrology and Earth System Sciences (by EGU)
(03) Hydrological Research Letters (by JSHWR)(01) Journal of Agricultural Meteorology (by SAMJ)(02) Journal of Advances in Modeling Earth Systems (by AGU)

7

(02) Journal of Geophysical Research – Atmospheres (by AGU)
(03) Journal of Hydrology (by Elsevier)(08) Journal of Meteorological Society of Japan (by MSJ)
(01) Meteorological Applications (by RMS)
(01) Meteorology and Atmospheric Physics (by Springer)(08) Monthly Weather Review (by AMS)
(05) Nonlinear Processes in Geophysics (by EGU)
(02) Quarterly Journal of the Royal Meteorological Society (by UK’s RMS)
(04) Scientific Online Letters on the Atmosphere (by MSJ)
(01) Tellus (by MISU Stockholm U.)(01) ながれ(14) 土木学会・水工学論文集(01) 土木学会・地球環境シンポジウム論文(01) 土木学会・AI&データサイエンス論文集(04) 水文・水資源学会誌(01) 土木学会論文集(01) 統計数理# Last Update: Oct. 2023

Affiliations
Japan Geoscience Union
Japan Society of Civil Engineers
Japan Society of Hydrology and Water Resources
Meteorological Society of JapanThe Remote Sensing Society of Japan

Editorship/Committee Member of Scientific/Social Activities
2017/01–2018/12 Columns by Senior Researchers, Japan Society of Hydrology and Water
Resources
2015/01–2016/12 Columns by Young Researchers, Japan Society of Hydrology and Water
Resources82015/06–2017/05 Associate Member, Committee on Hydroscience and Hydraulic Engineering, JJapan Society of Civil Engineers

Awards to Supervised Researchers/Students
2023/08

Outstanding Presentation Award, MSF DA Summer School in 2023
河﨑文俊(M2)

2023/02

Paper Encouragement Award, Committee on Hydraulic Engineering ,Japan
Society of Civil Engineers
藤村健介(M1) “降雨流出氾濫モデルのアンサンブルデータ同化安定化に関する研究”

2022/10

Outstanding Presentation Award, JSHWR‐JAHS Joint Symposium in 2022
塩尻大也(PD) “スパースセンサ位置最適化手法を活用した効率的な雨量計位
置決定”

Internal Awards to Supervised Researchers/Students
2023/12

Outstanding Presentation Award, Remote Sensing Course, Chiba University
河崎文俊(M2) “量子アニーリングを用いたデータ同化手法の開発”

2023/12

Outstanding Presentation Award, Remote Sensing Course, Chiba University
島袋隆也(M1) “深層学習モデルによる浸水深時空間分布のエミュレート手法
開発”

2023/02

Outstanding Presentation Award, Remote Sensing Course, Chiba University
大石健(M2) “Sinkhorn アルゴリズムを用いて高速化された局所粒子フィルタの開発”

2022/12

Outstanding Presentation Award, Remote Sensing Course, Chiba University
齋藤匠(M1) “特異ベクトル空間におけるデータ同化と観測位置決定手法への応用”

2021/12

Outstanding Presentation Award, Remote Sensing Course, Chiba University
土屋建(M1) “GradCAM を用いた熱帯低気圧画像識別器の高度化”

Outreach2018/11–Present JAXA Real‐time Weather Watch (contributing as a developer)
https://www.eorc.jaxa.jp/theme/NEXRA/index_e.htm
2017/05–2019/10 Weather Forecaster, RIKEN Weather Forecast
https://weather.riken.jp/index.html

Brief Narrative Summary
9

Dr. Shunji Kotsuki is a Professor of Institute for Advanced Academic Research (IAAR), 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 Postdoctoral Researcher (2014-2017), and Research Scientist (2017-2019) at RIKEN Center for
Computational Science (R-CCS). He started leading his research laboratory at CEReS, Chiba University
since November, 2019. He became to be a Professor of IAAR of Chiba U. since July 2022.
Dr. Kotsuki is a leading scientist on data assimilation and numerical weather prediction with over 10
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, andatmospheric and hydrological disaster predictions. His techniques for ensemble data assimilation havebeen incorporated in the RIKEN’s global atmospheric data assimilation system, and improved its weather
forecasts significantly. The NICAM-LETKF is running operationally as NEXRA since 2017 on the JAXA’s
supercomputing system.
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), RIKEN Ohbu Research
Incentive Award (2019), Chiba University Award for Distinguished Researcher (2020), and Young Scientist
Award of MEXT (2022). In 2017, Dr. Kotsuki was selected as an Excellent Young Researcher by Ministry of
Education, Culture, Sports, Science and Technology, Japan. He is also a visiting scientist of R-CCS, andexploring data-driven approaches for the environmental prediction science.

10

Publications: Peer‐reviewed Articles (*; Corresponding Author)[42].

Hu, J., Kotsuki, S., Igarashi, Y., Yang, Z., Talerko, M., Tischenkof, O., Protsak, V. and Kirieiev, S.
(2024): A tuning-free moderate scale burned area detection algorithm — A case study inChornobyl contaminated region. Int. J. Remote Sens. (accepted)[41].

Kurosawa, K., Kotsuki, S.*, and Miyoshi, T. (2023): Comparative Study of Strongly and Weakly
Coupled Data Assimilation with a Global Land-Atmosphere Coupled Model. Nonlin. Processes
Geophys., 30, 457-479. doi: 10.5194/npg-30-457-2023[40].

Muto, Y., Kanemaru, K., and Kotsuki, S. (2023): Correcting GSMaP through histogram matching
against satellite-borne radar-based precipitation. SOLA, 19, 217-224. doi: 10.2151/sola.2023-028[39].

Oishi, K., and Kotsuki, S.* (2023): Applying the Sinkhorn Algorithm for Resamling of Local Particle
Filter. SOLA, 19, 185-193. doi: 10.2151/sola.2023-024[38].

Ouyang, M., Tokuda, K., and Kotsuki, S.* (2023): Reducing manipulations in a control simulation
experiment based on instability vectors with the Lorenz-63 model. Nonlin. Processes Geophys., 30,
183–193. doi: 10.5194/npg-30-183-2023[37].

Hu, J., Igarashi, Y., Kotsuki, S.*, Yang, Z., Talerko, M., Landin, V., Tischenko, O., Zheleznyak, M.,
Protsak, V., and Kirieiev, S. (2023): Application of a tuning-free burned area detection algorithm to
the Chornobyl wildfires in 2022. Sci. Rep., 13, 5236. doi: 10.1038/s41598-023-32300-5[36].

Kotsuki, S.*, Terasaki, K., Satoh, M., and Miyoshi, T. (2023): Ensemble-based Data Assimilation of
GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation with the Nonhydrostatic
Icosahedral Atmospheric Model (NICAM), J. Geophys. Res., 128, e2022JD037447. doi:
10.1029/2022JD037447[35].

Momoi, M., Kotsuki, S., Kikuchi, R., Watanabe, S., Yamada, M., and Abe, S. (2023): Emulating
rainfall-runoff-inundation model using deep neural network with dimensionality reduction.
Artificial Intelligence for the Earth Systems, 2, 1-25. doi: 10.1175/AIES-D-22-0036.1[34].

Ouyang, M., Kotsuki, S., Ito, Y., and Tokunaga, T. (2022): Employment of hydraulic model and social
media data for flood hazard assessment in an urban city. J. Hydrol. Reg. Stud., 44, 101261. doi:
10.1016/j.ejrh.2022.101261[33].

Kotsuki, S.*, Miyoshi, T., Kondo K., and Potthast R. (2022): A Local Particle Filter and Its Gaussian
Mixture Extension Implemented with Minor Modifications to the LETKF. Geosci. Model Dev., 15,8325-8348. doi: 10.5194/gmd-15-8325-2022[32].

Kotsuki, S.*, and Bishop, H. C. (2022): Implementing Hybrid Background Error Covariance into
the LETKF with Attenuation-based Localization: Experiments with a Simplified AGCM. Mon. Wea.
Rev., 150, 283-302. doi:10.1175/MWR-D-21-0174.1[31].

Miyoshi, T., Terasaki, K., Kotsuki, S., Otsuka, S., Chen, Y.-W., Kanemaru, K., Okamoto, K., Kondo, K.,
Lien, G.-Y., Yashiro, H., Tomita, H., Sato, M., and Kalnay, E. (2022): Enhancing data assimilation of
GPM observations. Precipitation Science, Measurement Remote Sensing, Microphysics, and Modeling.
Elsevier, 787-804. doi:10.1016/B978-0-12-822973-6.00020-2[30].

Arakida, H., Kotsuki, S., Otsuka, S., Sawada, Y., and Miyoshi, T. (2021): Regional-scale data
assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIBDGVM) over Siberia. Prog. Earth Planet. Sci. 8:52. doi:10.1186/s40645-021-00443-6

11

[29].

Taler, J., Okazaki, A., Honda, T., Kotsuki, S., Yamaji, M., Kubota, T., Oki, R., Iguchi, T., and Miyoshi, T.
(2021): Oversampling Reflectivity Observations from a Geostationary Precipitation Radar
Satellite: Impact on Typhoon Forecasts within a Perfect Model OSSE Framework. J. Adv. Modeling
Earth Syst., 13, e2020MS002332. doi:10.1029/2020MS002332[28].

Carrio, D. S., Bishop, C. H. and Kotsuki, S. (2021): Empirical determination of the covariance of
forecast errors: an empirical justification and reformulation of Hybrid covariance models. Q. J. R.
Meteorol. Soc. 147, 2033-2052. doi:10.1002/qj.4008[27].

Watanabe, S., Kotsuki, S., Kanae, S., Tanaka, K. and Higuchi, A. (2020): Snow water scarcity
induced by the record-breaking warm winter in 2020 in Japan. Sci. Rep., 10, 18541.
doi:10.1038/s41598-020-75440-8.

[26].

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. Q. J. R. Meteorol. Soc., 146,
3399-3415. doi: 10.1002/qj.3852[25].

Miyoshi, T., Kotsuki, S., Terasaki, K., Otsuka, S., Lien, G.-Y., Yashiro, H., Tomita, H., Satoh, M., andKalnay, E. (2020): Precipitation Ensemble Data Assimilation in NWP Models. Satellite Precipitation
Measurement. Advances in Global Change Research, 69, Springer, 983-991. doi:10.1007/978-3030-35798-6_25[24].

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[23].

Otsuka, S., Kotsuki, S., Ohhigashi, M., and Miyoshi, T. (2019): GSMaP RIKEN Nowcast: Global
precipitation nowcasting with data assimilation. J. Meteor. Soc. Japan, 97, 1099-1117.
doi:10.2151/jmsj.2019-061[22].

Okazaki, A., Honda, T., Kotsuki, S., Yamaji, M., Kubota, T., Oki, R., Iguchi, T., and Miyoshi, T. (2019):
Simulating precipitation radar observations from a geostationary satellite. Atmos. Meas. Tech., 12,3985-3996. doi: 10.5194/amt-2018-278[21].

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[20].

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[19].

Terasaki, K., Kotsuki, S., Miyoshi, T. (2019): Multi-year analysis using the NICAM-LETKF data
assimilation system. SOLA, 15, 41-46. doi: 10.2151/sola.2019-009[18].

Kotsuki, S.*, Terasaki, K., Kanemaru, K., Satoh, M., Kubota, T. and Miyoshi, T. (2019): Predictabilityof Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Nearreal-time Global Atmospheric Data Assimilation System NEXRA. SOLA, 15A, 1-7. doi:
10.2151/sola.15A-001[17].

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 Casewith the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite
Mapping of Precipitation Data. J. Geophys. Res., 123, 7375-7392. doi: 10.1029/2017JD028092

12

[16].

Honda, T., Kotsuki, S., Lien, G.-Y., Okamoto, K. and Miyoshi, T. (2018): Assimilation of Himawari-8
All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction. J. Geophys.
Res., 122, 1-12. doi:10.1002/2017JD027096[15].

Kotsuki, S.*, Greybush, S., and Miyoshi, T. (2017): Can we optimize the assimilation order in theserial ensemble Kalman filter? A study with the Lorenz-96 model. Mon. Wea. Rev., 145, 4977-4995.
doi: 10.1175/MWR-D-17-0094.1[14].

Arakida, H., Miyoshi, T., Ise, T., Shima, S.-I., and Kotsuki, S. (2017): Non-Gaussian data assimilationof satellite-based Leaf Area Index observations with an individual-based dynamic global
vegetation model. Nonlin. Processes Geophys., 24, 553-567. doi:10.5194/npg-24-553-2017[13].

Grippa, M., Kergoat, L., Boone, A., Peugeot, C., Demarty, J., Cappelaere, B., Gal, L., Hiernaux, P.,
Mougin, E., Ducharne, A., Dutra, E., Anderson, M., Hain, C., and ALMIP2 Working Group (2017):
Modeling surface runoff and water fluxes over contrasted soils in pastoral Sahel: evaluation of the
ALMIP2 land surface models over the Gourma region in Mali. J. Hydrometeoro., 18, 1847-1866.
doi:10.1175/JHM-D-16-0170.1[12].

Getirana, A., Boone, A., Peugeot, C., and ALMIP2 Working Group (2017): Streamflows over a West
African basin from the ALMIP-2 model ensemble. J. Hydrometeoro., 18, 1831-1845.
doi:10.1175/JHM-D-16-0233.1[11].

Kotsuki, S.*, Ota, Y., and Miyoshi, T. (2017): Adaptive covariance relaxation methods for ensemble
data assimilation: Experiments in the real atmosphere. Q. J. R. Meteorol. Soc., 143, 2001-2015. doi:
10.1002/qj.3060[10].

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. J. Geophys. Res., 122, 1-20. doi:10.1002/2016JD025355[9].

Otsuka, S., Kotsuki, S., Miyoshi, T. (2016): Nowcasting with data assimilation: a case of Global
Satellite Mapping of Precipitation. Wea. and Forecasting, 31, 1409-1416. doi:10.1175/WAF-D-160039.1[8].

Kotsuki, S.*, and Tanaka, K. (2015): SACRA - a method for the estimation of global high-resolution
crop calendars from a satellite-sensed NDVI. Hydrol. Earth Syst. Sci., 19, 4441-4461. doi:
10.5194/hess-19-4441-2015[7].

Kotsuki, S.*, Takenaka, H., Tanaka, K., Higuchi, A. and Miyoshi, T. (2015): 1-km-resolution land
surface analysis over Japan: Impact of satellite-derived solar radiation. Hydrol. Res. Lett., 9(1), 1419. doi: 10.3178/hrl.9.14[6].

Kotsuki, S.*, Terasaki, K., and Miyoshi, T. (2014): GPM/DPR Precipitation Compared with a 3.5km-resolution NICAM Simulation. SOLA, 10, 204-209. doi:10.2151/sola.2014-043[5].

Kotsuki, S.*, Tanaka, K. and Watanabe, S. (2014): Projected hydrological changes and theirconsistency under future climate in the Chao Phraya River Basin using multi-model and multiscenario of CMIP5 dataset. Hydrol. Res. Lett., 8(1), 27-32. doi:10.3178/hrl.8.27[4].

Watanabe, S., Hirabayashi, Y., Kotsuki, S., Hanasaki, N., Tanaka, K., Mateo, C.M., Kiguchi, M., Ikoma,E., Kanae, S., Oki, T. (2014): Application of performance metrics for climate models to project
future river discharge in Chao Phraya River Basin. Hydrol. Res. Lett., 8(1), 33-38.
doi:10.3178/hrl.8.33

13

[3].

Kotsuki, S.*, and Tanaka, K. (2013): Uncertainties of precipitation products and their impacts on
runoff estimates through hydrological land surface simulation in Southeast Asia. Hydrol. Res. Lett.,
7(4), 79-84. doi: 10.3178/hrl.7.79[2].

Kotsuki, S.*, and Tanaka, K. (2013): Impacts of Mid-Rainy Season Rainfall on Runoff into the Chao
Phraya River, Thailand. J. Disaster Res., 8(3), 397-405. doi:10.20965/jdr.2013.p039[1].

Kotsuki, S.*, and Tanaka, K. (2013): Estimation of Climate Change Impact on Japanese Rice Yield
and Water Resources. Proceedings of 2013 IAHR World Congress, A10344.

Publications: Conference Paper (*; Corresponding Author)[1].

Sakaino, H., Gaviphatt, N., Insisiengmay, A., Zamora, L., Ningrum, D. F., and Kotsuki, S. (2023).
DeepTrCy: Life Stage Identification of Satellite Tropical Cyclone Images. IEEE International
GeoscienceandRemoteSensing
Symposium(IGARSS),
5186-5189.
doi:
10.1109/IGARSS52108.2023.10282363

Publications: Non Peer‐reviewed Articles[1].

Kotsuki, S.*, and Tanaka, K. (2013): Long-term Water Balance Analysis Using Different
Precipitation Products in Upper Chao Phraya River, Thailand. Proceedings of 6th APHW conference.

Invited Presentations[14]

Kotsuki, S., Shiojiri, D., Ouyang, M., Muto, Y., and Kanemaru, K.: Improving Global Precipitation
Estimates by Hydrological Land Data Assimilation. AMS Annual Meeting, January 11, 2023. (Jan.
11, Colorado Convention Center, Denver / online)[13]

Kotsuki, S., Ouyang, M., Saito, T. and Shiojiri, D.: Combining Data Assimilation and Sparse Sensing
Placement Method For Designing Better Observing Networks. RIKEN Data Assimilation Seminar,
September 14, 2022. (Sep. 14, online)[12]

Kotsuki, S. and Ouyang, M.: Optimizing mobile observing placements for numerical weather
prediction: Idealized experiments with a simplified AGCM. AOGS 2022, August 1-5, 2022. (Aug. 03,
online)[11].

Kotsuki, S., Terasaki, K., Satoh, M. and Miyoshi, T.: Ensemble-Based Data Assimilation of GPM DPR
Reflectivity into the Nonhydrostatic Icosahedral Atmospheric Model NICAM. JpGU 2021, June 3-6,2021 (June 3, online)[10].

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, September
11, 2020, (Sep. 11, online)[9].

Kotsuki, S., Pensoneault, A., Okazaki, A. and Miyoshi, T.: Weight Structure of the Local Ensemble
Transform Kalman Filter: A Case with an Intermediate AGCM. JpGU-AGU Joint Meeting 2020, July
12-16, 2020. (July 13, Makuhari Messe, Chiba)

14

[8].

Kotsuki, S.: Leading Research on Data Assimilation in Global Weather Prediction System.
Topological Data Analysis Laboratory Seminar, Apr. 29, 2020. (Apr. 29, Kyoto-Univ., Zoom)[7].

Kotsuki, S.: Introduction of Data Assimilation and Its Techniques. Topological Data Analysis
Laboratory Seminar, Apr. 29, 2020. (Apr. 29, Kyoto-Univ, on Zoom)[6].

Kotsuki, S.: Data Assimilation Research for Global Weather Prediction, The 7th International
Symposium on Water Environmental Systems, Nov. 15, 2019 (Nov. 15, Tohoku University, Sendai)[5].

Kotsuki, S.: Leading Research on Data Assimilation in Global Weather Prediction System. Earth
Science Department Seminar, Aug. 27, 2019. (Aug. 27, U. Melbourne, Melbourne)[4].

Kotsuki, S.: Improving Global Precipitation Forecasts using Satellite-derived Precipitation Data,
NWP seminar Bureau of Meteorology, Aug. 17, 2019. (Aug. 17, Australian Bureau of Meteorology,
Melbourne)[3].

Kotsuki, S., and Miyoshi, T.: Diagnosing Observation Impacts and Error Covariance with NICAMLETKF. LMU Data Assimilation Seminar, Jun. 18, 2019. (Jun. 18, Munich Univ., Munich)[2].

Kotsuki, S., and Miyoshi, T.: Diagnosing Observation Impacts and Error Covariance with NICAMLETKF. DWD NWP Seminar, Jun. 13, 2019. (Jun. 13, Deutscher Wetterdienst, Offenbach)[1].

Kotsuki, S., Sato, Y., Terasaki, K., Yashiro, H., Tomita, H., Satoh, M. and Miyoshi, T.: Model Parameter
Estimation with Data Assimilation using NICAM-LETKF. JpGU Meeting 2019, May 26-30, 2019.
(May 29, Makuhari Messe, Chiba)

15Appendix A: CV only in Japanese

Publications: Peer‐reviewed Articles in Japanese[21].

佐々木景悟，武藤裕花，塩尻大也，小槻峻司 (2023): ベイズ最適化を用いた降雨流出氾濫モデ
ルの計算効率性の高いパラメータ最適化に関する研究. AI・データサイエンス論文集, 4, 602610. doi: 10.11532/jsceiii.4.3_602[20].

関令法，塩尻大也，小槻峻司 (2023): 日本の降水量の次元圧縮を対象とした特異値分解と非負値行列因子展開の比較．AI・データサイエンス論文集, 4, 772-778. doi: 10.11532/jsceiii.4.3_772[19].

島袋隆也，塩尻大也，小槻峻司 (2023): 深層学習モデルによる浸水深時空間分布のエミュレー
ト手法開発． AI・データサイエンス論文集, 4, 553-560. doi: 10.11532/jsceiii.4.3_553[18].

白石健太，武藤裕花，小槻峻司 (2023): 深層学習に基づく超解像技術を用いた降水量データの高 解 像 度 化 に 関 す る 研 究 ．AI・ デ ー タ サ イ エ ン ス 論 文 集, 4, 515-521. doi:
10.11532/jsceiii.4.3_515[17].

武藤裕花，塩尻大也，小槻峻司 (2023): 局所アンサンブルデータ同化を用いた地上雨量観測からの全球降水分布の推定． 水工学論文集 (accepted)[16].

齋藤匠，小槻峻司*，Mao OUYANG，塩尻大也 (2022): スパースセンサ最適化を用いた大次元力
学 系 に お け る 有 効 な 観 測 位 置 決 定 手 法 の 開 発 ． 水 工 学 論 文 集, 78, 391-396. doi:
10.2208/jscejhe. 78.2_I_391[15].

藤村健介，小槻峻司*，山田真史，塩尻大也，渡部哲史 (2022): 降雨流出氾濫モデルのアンサ
ンブルデータ同化安定化に関する研究． 水工学論文集, 78, 409-414. doi: 10.2208/jscejhe.
78.2_I_409[14].

塩尻大也，小槻峻司，齋藤匠，Mao OUYANG (2022): スパースセンサ位置最適化手法による効率的な雨量計配置手法の開発． 水工学論文集, 78, 385-390. doi: 10.2208/jscejhe. 78.2_I_385[13].

赤塚洋介，瀬戸里枝，鼎信次郎，小槻峻司，渡部哲史 (2021): 豪雪地帯に位置するダム対象とした融雪期の操作における AI ダム操作モデルの応用可能性．水工学論文集, 77, 109-114.
doi:10.2208/jscejhe.77.2_I_109[12].

小槻峻司*，桃井裕広，菊地亮太，渡部哲史，山田真史，阿部紫織，綿貫翔 (2020): 回帰学習
器のアンサンブル学習による降雨洪水氾濫モデル・エミュレータ．水工学論文集, 76, 367-372.
doi:10.2208/jscejhe.76.2_I_367[11].

関本大晟，渡部哲史，小槻峻司，山田真史，阿部紫織，綿貫翔 (2020): 降雨流出氾濫モデル・エ ミ ュ レ ー タ に よ る 浸 水 範 囲 予 測. 水 工 学 論 文 集, 76,
547-552. doi:10.2208/
jscejhe.76.2_I_547[10].

阿部紫織, 渡部哲史, 山田真史, 小槻峻司，綿貫翔 (2019): 大規模気候予測情報を用いた浸水解析 に 降 水 量 観 測 値 と 海 面 水 温 パ タ ー ン が 及 ぼ す 影 響. 水 工 学 論 文 集, 75, 1081-1086.
doi:10.2208/jscejhe.75.2_I_1081

16

[9].

田中智大，渡部哲史，小槻峻司，林義晃，丸谷靖幸，峠嘉哉，山崎大，木村匡臣，田上雅浩，
江草智弘，橋本雅和，仲吉信人 (2018): 最前線の水文・水資源学 ～WACCA 世代の挑戦～，水文・水資源学会誌，31(6), 509-540. doi: 10.3178/jjshwr.31.509[8].

小槻峻司*，田中賢治 (2014): 衛星観測植生指標を活用した全球農事暦プロダクトの高精度化.
土木学会論文集 B1, 70(4), 259-264. doi: 10.2208/jscejhe.70.I_259[7].

小槻峻司*，田中賢治，小尻利治 (2013): 気候変動が日本の水資源に与える影響推計 (Ⅱ) -水需
給・米生産変化とその適応策-. 水文・水資源学会誌, 26, 143-152. doi: 10.3178/jjshwr.26.143[6].

小槻峻司*，田中賢治，小尻利治 (2013): 気候変動が日本の水資源に与える影響推計 (I) -日本
全域水資源モデルの開発-. 水文・水資源学会誌, 26, 133-142. doi: 10.3178/jjshwr.26.133[5].

小槻峻司*，田中賢治，小尻利治 (2013): 多様な作物分布を考慮した全球農業水需要量推定. 環
境科学会誌, 26(2), 158-166. doi: 10.11353/sesj.26.158[4].

小槻峻司*，田中賢治 (2013): 陸面過程モデルと大気水収支法による灌漑農地からの水蒸気供給量推定. 土木学会論文集 B1, 69, 1801-1806. doi: 10.2208/jscejhe.69.I_1801[3].

小槻峻司*，田中賢治，小尻利治，浜口俊雄 (2012): 衛星データから作成した農事暦を活用した全球陸域水循環解析. 水文・水資源学会誌, 25, 373-388. doi: 10.3178/jjshwr.25.373[2].

小槻峻司*，田中賢治，小尻利治，浜口俊雄 (2012): 群知能最適化手法を用いた分布型流出モ
デルのパラメーター同定. 土木学会論文集 B1, 68, 523-528. doi: 10.2208/jscejhe.68.I_523[1].

小槻峻司*，田中賢治，小尻利治，浜口俊雄 (2011): 灌漑を考慮した陸域水循環モデルの構築.
水工学論文集, 55, 553-558. doi:10.2208/jscejhe.67.I_553

Publications: Non Peer‐reviewed Articles in Japanese[6].

滝野晶平，本田匠，三好建正，大東真利茂，小槻峻司，塚田智之，中田安彦(2021): 機械学習を活用した発電ダムの運用最適化システムの開発, 大ダム, No. 257(2021-10), 30-36.

[5].

渡部哲史，小槻峻司，鼎信次郎，田中賢治, 樋口篤志, (2020): 2020 年の記録的少雪に関する速報的分析, 水文・水資源学会誌, 第 33 号, 182-188.

[4].

小槻峻司，田中賢治 (2014): 西アフリカ乾燥域における AMSR-E 土壌水分プロダクトと陸面過程モデル解析値の比較. 2013 年土壌水分ワークショップ論文集, 33-36.

[3].

小槻峻司，峠嘉哉，萬和明，相馬一義，甲山治，田中賢治 (2013): SiBUC Mannual 利用編
ver1.0 ―Part1: モデル入力データの作成と陸面過程解析の方法―. 京都大学防災研究所年報, 第56 号 B, 567-584.

[2].

浜口俊雄，田中拓馬，小槻峻司，田中賢治，峠嘉哉，安倍雅宏 (2012): 分布型流出モデルに沿ったマクロスケールでの土砂生産・輸送モデリングの基礎的研究. 京都大学防災研究所年報, 第55 号 B, 501-509.

[1].

小槻峻司，田中賢治，小尻利治，浜口俊雄 (2011): 衛星データによる農事暦を活用した灌漑必
要水量の推定. 京都大学防災研究所年報, 第 54 号 B, 645-653.

17

Publications: Reports in Japanese[7].

小槻峻司, 寺崎康児, 新保明彦, 坂本雅巳, 藤田匡, 津口裕茂, 北畠尚子, 竹見哲也, 高薮縁, 金丸佳矢, 鼎信次郎, 中村尚, 富田浩文, 三好建正 (2019): 「平成 30 年 7 月豪雨に関する緊急対応研究会」の報告. 天気, 66(3), 253-259.

[6]

川畑拓矢，上野玄太，中野慎也，藤井陽介，三好建正，小守信正，増田周平，茂木耕作，小槻峻司，澤田洋平，Peter Jan van Leeuwen，長尾大道 (2019): 第 9 回データ同化ワークショッ
プの報告. 天気, 66(2), 51-54.

[5]

川畑拓矢，上野玄太，中野慎也，藤井陽介，三好建正，小守信正，増田周平，茂木耕作， 中
村和幸，杉本憲彦，前島康光，Le Duc，小槻峻司，須藤明人，杉浦望実，釜堀弘隆 (2018): 第
8 回データ同化ワークショップの報告. 天気, 65(5), 22-25.

[4]

田中智大，小槻峻司，中下慎也，田上雅浩，渡部哲史，丸谷靖幸，綿貫翔，柿沼太貴(2018):
WACCA meeting 07 会議報告「研究手法に着目した水関連研究の新たな発展可能性」. 水文・
水資源学会誌，31(1), 33-41. doi: 10.3178/jjshwr.31.33[3].

田中智大，渡部哲史， 小槻峻司，萬和明 (2017): 水文・水資源学会有志による学会意識アンケートの結果を通した若手・中堅世代の学会に対する認識について. 水文・水資源学会誌，
30(4), 245-259. doi: 10.3178/jjshwr.30.245[2].

渡部哲史，山崎大，渡辺恵，小槻峻司，五十嵐康記，仲吉信人，田上雅浩，木下陽平，池内
寛明，木島梨沙子，田中智大，吉田奈津妃，佐谷茜 (2015): 水文若手会 水関連分野次世代研究
探索ワークショップ(WACCA meeting 02)会議報告 -水関連分野における分野融合の可能性と意義-. 水文・水資源学会誌，28(6), 304-312. doi: 10.3178/jjshwr.28.304[1].

渡部哲史，瀧本浩史，仲吉信人，大泉伝，小槻峻司，峠嘉哉 (2011): 水文・水資源学会
若手会 活動報告書 2011. 水文・水資源学会誌，24(5), 300-306.

水文

Publications: Book Chapters in Japanese[2].

小槻峻司 (2022): 9.2.5 水と食料. 水文・水資源ハンドブック 第二版, 水文・水資源学会.
https://www.asakura.co.jp/detail.php?book_code=26174.

[1].

小槻峻司 (2022): 9.2 NEXRA を用いた予測可能性調査. 2018 年夏の異常気象, 気象研究ノート
第 246 号, 日本気象学会. https://www.metsoc.jp/publications/research-notes.

Invited Presentations (in Japanese)[18]

小槻峻司: A World Beyond Predictions:気象制御実現への挑戦, 千葉大学/株式会社リコー包括連
携 第二回包括連携協議会(講演会), 10/05, 2022. (Oct. 05, Online)[17]

小槻峻司: 大規模数値シミュレーションで切り開く豪雨・洪水予測研究, 第 35 回 JST 数学キャ
ラバン in 京都, 07/29, 2022. (Jul. 29, Online)

18

[16]

小槻峻司: 「観測の価値」を最大化するデータ同化・予測手法の開発, 異分野融合研究セミナ
ー, 07/19, 2022. (Jul. 19, Online)[15]

小槻峻司: 「観測の価値」を最大化するデータ同化・予測手法の開発, DNA 気候学 雲解像気候学セミナー, 07/19, 2022. (Jul. 19, Online)[14]

小槻峻司: データ同化と大規模数値シミュレーションで切り開く豪雨・洪水予測研究, 水工学
オンライン連続講演会, 04/20, 2022. (Apr. 20, Online)[13].

Kotsuki, S., and Bishop, H. C.: Implementing Hybrid Background Error Covariance into the LETKF
with Attenuation-based Localization: Experiments with a Simplified AGCM. 第 12 回データ同化ワ
ークショップ, 02/17, 2022, (Feb. 17, Online)[12]

小槻峻司: 数値計算と観測を融合するデータ同化 ー天気予報の仕組みと研究の最前線ー, あかりんアワー, 11/02, 2021. (Nov. 02, 千葉大付属図書館 N 棟 1 階)[11].

小槻峻司: フラックス観測データを活用したデータ同化とスパースセンシング, JapanFlux デー
タ利用・共同研究促進ワークショップ, 08/05, 2021. (Aug. 05, Online)[10].

小槻峻司: 「観測の価値」を最大化するデータ同化手法の開発：～最先端の天気予報研究より～, 設計に活かすデータ同化研究会, 02/19, 2021. (Feb. 19, Online)[9].

小槻峻司: Local Ensemble Transform Kalman Filter: introduction and practical techniques.
Mathematical Modelling Seminar, 12/22, 2020. (Dec. 22, Online)[8].

小槻峻司, 岡崎淳史, 樺山修: COVID-19 感染予測数理モデルのアンサンブルデータ同化：感染力と地球環境の関連分析．JST・感染症問題と環境・エネルギー分野に関するエキスパートセミ
ナー, 07/22, 2020. (Jul. 22, online)[8].

小槻峻司: リモートセンシングデータを使った天気予報研究の最前線．千葉大学経済人倶楽部・絆 公開講座, 02/21, 2020. (Feb. 21, 千葉大学アカデミックリンクセンター)[7].

小槻峻司: データ同化と経験科学の相似性について. 科学基礎論学会, 11/30, 2019 (Nov. 30, 日本
大学文理学部)[6].

小槻峻司, 三好建正: 全球大気データ同化システムによる天気予報研究の最前線．第 14 回名工
大・核融合研合同セミナー , 07/18, 2019. (Jul. 18, 名工大 2 号館)[5].

小槻峻司: 天気予報研究の最前線 -研究者は何に魅せられているか- ． 阪神高速道路株式会社・
堺建設部勉強会, 11/08, 2018. (Nov. 08, 阪神高速道路株式会社・堺建設部)[4].

小槻峻司, 黒澤賢太, 三好建正: EFSO の現状と惑星気象研究への発展の可能性． 第 19 回惑星圏研究会, 02/27-03/01, 2018. (Feb 27, 東北大学・青葉サイエンスホール)[3].

小槻峻司, 黒澤賢太, 三好建正: 全球大気データ同化システム NICAM-LETKF を使った EFSO 観測
インパクト推定． 第８回データ同化ワークショップ, 01/19, 2018. (Jan 19, 明治大学中野キャン
パス)

[2].

小槻峻司, 三好建正: 全球大気アンサンブルデータ同化システム NICAM-LETKF による衛星降水観測データ同化． 地震研特定共同研究(B)「データ同化」勉強会, 2017. (Jul 14, ，東大地震研
究所)

19

[1].

小槻峻司, 三好建正: 予測モデルのためのデータ同化． PSTEP 研究集会「太陽地球圏環境予測のためのモデル研究の展望」, 01/26-27, 2017. (Jan 27, 名古屋大学，名古屋)

千葉大学 マル合審査[2].

博士後期課程：マル合[1].

博士前期課程：マル合（2021 年 07 月）
博士後期課程：合（2019 年 12 月）

千葉大学大学院 融合理工学府 地球環境科学専攻 リモートセンシングコース 講義[3].

リモートセンシング特論Ⅱ (T1&T2; FY23)[2].

地球観測社会システム (分担; T4&T5; FY20, 21, 22, 23)[1].

地球環境科学専攻特別講義Ⅱ(分担; T1&T2; FY21, 22, 23)

千葉大学 工学部情報工学コース 講義[2].

量子力学基礎(1) (T4&T5; FY20, 21, 22, 23)[1].

リモートセンシング工学 (分担; T4&T5; FY19, 20, 21, 22, 23)

千葉大学 環境リモートセンシング研究センター 委員会 (赤字: 委員長)
[23].

中期計画推進, 自己点検・評価, 計算機および DB, 環境 ISO 実行, 広報, 予算, 学術推進企画小[22].

中期計画推進, 自己点検・評価, 計算機および DB, 環境 ISO 実行, 広報, 予算, 学術推進企画小[21].

中期計画推進, 自己点検・評価, 計算機および DB, 環境 ISO 実行, 広報, 予算, 学術推進企画小[20].

中期計画推進, 自己点検・評価, 計算機および DB, 環境 ISO 実行, 広報[19].

中期計画推進, 自己点検・評価

予算委員長としての活動
・HP リニューアル (https://ceres.chiba-u.jp)
・研究センター広報動画 (https://www.youtube.com/watch?v=-hdzeJl3QCw)
・ロゴ作成 (https://ceres.chiba-u.jp/ceres/logo)

千葉大学大学院 融合理工学府 リモートセンシングコース 委員会

20

[23].

研究推進・広報社会連携委員会[22].

研究推進・広報社会連携委員会

千葉大学 その他[23].

千葉大学・工学研究院

リサーチ・ハブリーダー（研究院長特別補佐）[21, 22].

科研費事前確認支援制度・支援教員

Teaching Experiences (at RIKEN)
Spring 2019

Data Assimilation A, a graduate- and undergraduate-level introductory data
assimilation course, Faculty of Science, Kyoto University

Spring 2018

Data Assimilation A, a graduate- and undergraduate-level introductory data
assimilation course, Faculty of Science, Kyoto University

Fall 2017

Data Assimilation B, a graduate- and undergraduate-level advanced data
assimilation course, Faculty of Science, Kyoto University

Spring 2017

Data Assimilation A, a graduate- and undergraduate-level introductory data
assimilation course, Faculty of Science, Kyoto University

Spring 2016

Special Lecture on Mathematical Science: Data Assimilation, a graduate- andundergraduate-level introductory data assimilation course, Faculty of Science,
Kyoto University

September 2016

iTHES School on Data Assimilation, RIKEN iTHES
http://www.data-assimilation.riken.jp/jp/events/ithes_da_2016fall/Student Interns Supervised (at RIKEN)[5].

Aulia Febianda Anwar Tinumbang, Kyoto University (RIKEN CCS internship program, 2018)[4].

Andrew Pensoneault, University of Iowa (RIKEN CCS internship program, 2018)[3].

Takuya Kurihana, University of Tsukuba (RIKEN AICS internship program, 2017)[2].

Taiga Shibata, University of the Ryukyus (RIKEN AICS internship program, 2017)[1].

Yaping Chang, University of Chinese Academy of Sciences (RIKEN International Program Associate,
2015)

21

Appendix‐B: Publications: Peer‐reviewed Articles in Japanese (English)[16].

Saito, T., Kotsuki, S.*, Ouyang, M. and Shiojiri, D. (2022): Improving the data-driven sparse sensor
placement method for large-dimensional dynamical systems. Ann. J. Hydraulic Engineering, XX, XXXX. doi: 10.2208/jscejhe.XXXX (in press)[15].

Fujimura, K., Kotsuki, S.*, Yamada, M., Shiojiri, D. and Watanabe, S. (2022): Exploring appropriate
inflation and localization methods to stabilize ensemble data assimilation of a rainfall-runoffinundation mode. Ann. J. Hydraulic Engineering, XX, XX-XX. doi: 10.2208/jscejhe.XXXX (in press)[14].

Shiojiri, D., Kotsuki, S., Saito, T., and Ouyang, M. (2022): Investigating effective rain gauge locations
through the data-driven sparse sensor placement method. Ann. J. Hydraulic Engineering, XX, XX-XX.
doi: 10.2208/jscejhe.XXXX (in press)[13].

Akatsuka, Y., Seto, R., Kanae, S., Kotsuki, S. and Watanabe, S. (2021): A study on the applicability of
AI dam operation model in snowmelt season for dams in heavy snowfall area. Ann. J. HydraulicEngineering, 77, 109-114. doi:10.2208/jscejhe.77.2_I_109[12].

Kotsuki, S.*, Momoi, M., Kikuchi, R, Watanabe, S., Yamada, M., Abe, S., and Watanuki A. (2020):
Emulating rainfall-runoff-inundation model through ensemble learning of multiple regularized
regressors. Ann. J. Hydraulic Engineering, 76, 367-372. doi:10.2208/jscejhe.76.2_I_367[11].

Sekimoto, T., Watanabe, S., Kotsuki, S., Yamada, M., Abe, S. and Watanuki, A. (2020): Predicting
flood inundation area by rainfall-runoff-inundation model emulator． Ann. J. HydraulicEngineering, 76, 547-552. doi:10.2208/jscejhe.76.2_I_547[10].

Abe, S., Watanabe, S., Yamada, M., Kotsuki, S. and Watanuki, A. (2019): Impact on observed rainfall
and patterns of sea surface temperature on flood analysis using massive climate prediction data.
Ann. J. Hydraulic Engineering, 75, 1081-1086. doi:10.2208/jscejhe.75.2_I_1081[9].

Tanaka, T.，Watanabe, S., Kotsuki, S., and Coauthors (2018): Frontiers in hydrology and water
resources research - WACCA generation’s challenges. J. Japan Soc. Hydrol. Wat. Resourc., 31(6),509-540. doi: 10.3178/jjshwr.31.509[8].

Kotsuki, S.*, and Tanaka, K. (2014): Improvement of global crop calendar product using satellitesensed vegetation indexes. Ann. J. Hydraulic Engineering, 70(4), 259-264. doi:
10.2208/jscejhe.70.I_259[7].

Kotsuki, S.*, Tanaka, K. and Kojiri, T. (2013): Estimation of climate change impact on Japanesewater resources Part II: Water demand‒supply balance, rice yield changes, and an adaptation plan,
J. Japan Soc. Hydrol. Wat. Resourc., 26, 143-152. doi: 10.3178/jjshwr.26.143[6].

Kotsuki, S.*, Tanaka, K. and Kojiri, T. (2013): Estimation of climate change impact on Japanesewater resources: Part I The development of a Japanese water resource model, J. Japan Soc. Hydrol.
Wat. Resourc., 26, 133-142. doi: 10.3178/jjshwr.26.133[5].

Kotsuki, S.*, Tanaka, K. and Kojiri, T. (2013): An estimation of global agricultural water demand
including spatial distribution of crop species. Environ. Sci., 26(2), 158-166. doi:
10.11353/sesj.26.158[4].

Kotsuki, S.*, and Tanaka, K. (2013): Estimating vapor supply from irrigated cropland usinghydrological land surface model and atmospheric water balance method. Ann. J. HydraulicEngineering, 69, 1801-1806. doi: 10.2208/jscejhe.69.I_1801

22

[3].

Kotsuki, S.*, Tanaka, K., Kojiri, T. and Hamaguchi, T. (2012): Simulation of global water cycle inland using a crop calendar specified by phenological analysis of NDVI. J. Japan Soc. Hydrol. Wat.
Resourc., 25, 373-388. doi: 10.3178/jjshwr.25.373[2].

Kotsuki, S.*, Tanaka, K., Kojiri, T. and Hamaguchi, T. (2012): Parameter identification of
distributed runoff model using the particle swarm optimization method. Ann. J. HydraulicEngineering, 68, 523-528. doi: 10.2208/jscejhe.68.I_523[1].

Kotsuki, S.*, Tanaka, K., Kojiri, T. and Hamaguchi, T. (2011): Development of water circulation
model
includingirrigation.
Ann.
J.
HydraulicEngineering,
55,
553-558.
doi:10.2208/jscejhe.67.I_553

水工学論文集  Ann. J. Hydraulic Engineering
水文・水資源学会誌  J. Japan Soc. Hydrol. Wat. Resourc.
環境科学会誌  Environ. Sci.

23