Kenta Kurosawa, Ph.D.
# kurosawa@chiba-u.jp
 http://kenta9638.com/

Education
Sep. 2019 – Sep. 2023]

Ph.D. Atmospheric and Oceanic Science, University of Maryland, CollegePark, MD, USA.
Thesis title: Bridging Gaussian and non-Gaussian Data Assimilation for HighDimensional Geophysical Models.

Apr. 2016 – Mar. 2018]

M.Eng. Civil Engineering, Kobe University, Japan.
Thesis title: Development of an estuarine reanalysis-forecasting system with 3DVAR
assimilation for the Seto Inland Sea.

Apr. 2012 – Mar. 2016]

B.Eng. Civil Engineering, Kobe University, Japan.
Thesis title: Development of a weather routing system optimized for ship navigation
based on oceanic and atmospheric datasets coupled with graph theory.

Research Interests
Data Assimilation, Numerical Weather Prediction, Numerical Simulations, Control Theory, Datadriven Forecasting

Professional ExperienceJan. 2024 – · · · ·]Postdoctoral Researcher, Center for Environmental Remote Sensing, Chiba
University, Chiba, Japan.

Oct. 2023 – Dec. 2023]

Postdoctoral Researcher, Department of Atmospheric and Oceanic Science,
University of Maryland, College Park, MD, USA.

Sep. 2019 – Sep. 2023]

Research Assistant, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA.

Apr. 2018 – Aug. 2019]Technical Staﬀ, Data Assimilation Research Team, RIKEN, Kobe, Japan.

Oct. 2016 – Mar. 2018]

Research Assistant, Data Assimilation Research Team, RIKEN, Kobe, Japan.

Research Publications
Refereed journal publications
1

K. Kurosawa, S. Kotsuki, and T. Miyoshi, “Comparative study of strongly and weakly coupled data
assimilation with a global land–atmosphere coupled model,” Nonlin. Processes Geophys., vol. 30, no. 4,
pp. 457–479, 2023.  doi: 10.5194/npg-30-457-2023.

2

K. Kurosawa and J. Poterjoy, “A statistical hypothesis testing strategy for adaptively blending particle
ﬁlters and ensemble Kalman ﬁlters for data assimilation,” Mon. Wea. Rev., vol. 151, no. 1, pp. 105–125,2023.  doi: 10.1175/MWR-D-22-0108.1.

3

K. Kurosawa and J. Poterjoy, “Data assimilation challenges posed by nonlinear operators: A
comparative study of ensemble and variational ﬁlters and smoothers,” Mon. Wea. Rev., vol. 149, no. 7,
pp. 2369–2389, 2021.  doi: 10.1175/MWR-D-20-0368.1.

4

K. Kurosawa, Y. Uchiyama, and T. Kosako, “Development of a numerical marine weather routing
system for coastal and marginal seas using regional oceanic and atmospheric simulations,” OceanEngineering, vol. 195, p. 106 706, 2020, issn: 0029-8018.  doi: 10.1016/j.oceaneng.2019.106706.

5

S. Kotsuki, K. Kurosawa, and T. Miyoshi, “On the properties of ensemble forecast sensitivity toobservations,” Quarterly Journal of the Royal Meteorological Society, vol. 145, no. 722, pp. 1897–1914, 2019.
 doi: 10.1002/qj.3534.

6

S. Kotsuki, K. Kurosawa, S. Otsuka, K. Terasaki, and T. Miyoshi, “Global precipitation forecasts bymerging extrapolation-based nowcast and numerical weather prediction with locally optimizedweights,” Weather and Forecasting, vol. 34, no. 3, pp. 701–714, 2019.  doi: 10.1175/WAF-D-18-0164.1.

Journal papers in Japanese
1

H. Wang, K. Kurosawa, and Y. Uchiyama, “Development of a data assimilation system based on
ensemble kalman ﬁlter and its application to the seto inland sea,” vol. 77, Nov. 2021, pp. 385–390.  doi:
10.2208/kaigan.77.2_I_385.

2

Y. Uchiyama, N. Sengo, and K. Kurosawa, “Development of a hycom-roms downscaling ocean modeland its application to the south china sea,” vol. 74, Jan. 2018, pp. 625–630.  doi:
10.2208/kaigan.74.I_625.

3

K. Kurosawa, Y. Uchiyama, and T. Miyoshi, “On improvement of an estuarine reanalysis-forecast
model for the seto inland sea based on 3d variational assimilation,” 2, vol. 73, Japan Society of Civil
Engineers, 2017, pp. 1663–1668.  doi: 10.2208/kaigan.73.i_1663.

4

Y. Uchiyama, N. Okada, and K. Kurosawa, “Eddy analysis in the north paciﬁc using an eddy-trackingalgorithm,” 2, vol. 73, 2017, pp. 1429–1434.  doi: 10.2208/kaigan.73.I_1429.

5Y. Uchiyama, K. Kurosawa, T. Kosako, and H. Tada, “Development of a weather routing system basedon graph theory coupled with a compact ocean model for optimal vessel navigation,” vol. 72, Nov. 2016,
pp. 1549–1554.  doi: 10.2208/kaigan.72.I_1549.

Awards and Fellowships
2023]

Outstanding Publication Award, University of Maryland.

2022]

Green Fund Scholarship Award, University of Maryland.

]

NCAR Advanced Study Program (ASP), Graduate Visitor Program, National Center for
Atmospheric Research.

2019]

Dean’s Fellowship from the College of Computer, Mathematical, and Natural Sciences,
University of Maryland.

2018

]

Full Exemption from Repayment of Japan Student Services Organization ( JASSO) TypeI Scholarship for Fiscal Year 2018, Japan .