14th Data Assimilation workshop


Group photo

Registration/Poster presentation/Icebreaker

  • Registration formClosed
  • Deadline: 31st July 2023

*The registration form is the same as the IMT-A & KU & RIKEN joint Data Assimilation workshop (9:00-12:00 29th Aug.) .

*Call for only poster presentation (Please submit the title only)

*Icebreaker is an optional event with charge of ¥1000. Please pay "cash" at the registration desk on-site. Cancellation can be accepted by 12:00 JST 21th August by sending an email to the desk (See email address at the bottom). After that, the icebreaker fee will be charged regardless of your attendance.


Data Assimilation (DA) combines simulation and observation based on dynamical systems theory and statistical methods and is used for making a better prediction, creating useful analysis datasets, evaluating observing networks, and estimating tunable model parameters among many other useful outcomes. It is applied to a variety of research fields such as geoscience, engineering, and biological science. The progress of DA theories is essential in improving the analyses and forecasts and in expanding DA’s broader impacts.
This workshop aims to share the recent progress of DA research in broader applications and to discuss future challenges and prospects. We invite two world’s renowned scientists who are also invited to ICIAM2023 in Tokyo in the preceding week (20-25 August). In addition, to promote exchanges and interactions among researchers in various fields, this workshop will be held on the same day right after the IMT-Atlantique & Kyoto University & RIKEN joint data assimilation workshop.


29th August

Time Speaker Title
13:00-13:30 Registration
[Chair: J. Liang]
Takemasa Miyoshi
Opening remarks
13:50-15:00 Keynote: Craig Bishop
(University of Melbourne)
Bounded variable data assimilation
15:00-15:50 Poster presentation
(Odd number)
15:50-16:05 Fugaku Tour
16:05-16:25 Break
[Chair: L. Li]
James Taylor
Improving forecasts of multi-scale convective systems with the assimilation of radar observations
17:05-17:45 Invited: Juan Ruiz
Machine learning-based estimation of state-dependent forecast uncertainty: application to data assimilation
17:45-18:30 Icebreaker

30th August

Time Speaker Title
09:00-09:30 Registration
[Chair: J. Taylor]
Keynote: Pierre Tandeo
Data-driven reconstruction of partially observed dynamical systems
10:40-11:30 Poster presentation
(Even number)
11:30-12:10 Le Duc
(University of Tokyo)
Unbalanced optimal transport: a new look into ensemble forecast and data assimilation
12:10-13:30 Lunch Break [restarurant] [kitcken car]
[Chair: M. Goodliff]
Shigeru Fujita
Fundamental research for the reanalysis data of the space weather based on the global MHD simulation
14:10-14:50 Shun Ohishi
LETKF-based Ocean Research Analysis (LORA): A new ensemble ocean analysis dataset
14:50-15:10 Break
[Chair: Y. Maejima]
Nozomi Sugiura
Global ocean data assimilation based on the comparison of the path signatures of model and observed profiles
15:50-16:00 Takemasa Miyoshi
Closing remarks

Poster presentation

Number Speaker Title
P01 Dai Tie
(Institute of Atmospheric Physics)
Improving aerosol optical properties and clear-Sky solar power prediction by assimilating geostationary satellite observations
P02 Xiaoxing Wang
(Research Organization of Information and Systems)
Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction
P03 Shinya Nakano
Emulator of global MHD simulation of magnetosphere-ionosphere system and data assimilation
P04 Masanobu Inubushi
(Tokyo University of Science)
Characterizing data assimilation in Navier–Stokes turbulence with transverse Lyapunov exponents
P05 Masahiro Tanoue
Data assimilation of water isotopes using NICAM-LETKF
P06 Kazuyoshi Suzuki
Assimilation of AMSR2 sea surface wind data into the regional climate reanalysis system - A case study of an winter extreme precipitation event in interior Alaska -
P07 Akira Yamazaki
AFES-LETKF experimental ensemble reanalysis version 3 (ALERA3)
P08 Kota Takeda
(Kyoto University)
Mathematical analysis of the ensemble transform Kalman filter with covariance inflation
P09 Saori Nakashita
(Kyoto University)
Mesoscale ensemble assimilation of dense upper observations from three research vessels
P10 Takeshi Enomoto
(Kyoto University)
Convergence properties of the conjugate-gradient and Newton methods
P11 Jianyu Liang
Using Bred vectors to understand the instabilities in Venus's atmosphere
P12 Jianyu Liang
A machine learning approach to the observation operator for satellite radiance data assimilation
P13 Rakesh Teja Konduru
Addressing imbalance-related challenges in hourly updated satellite radiance data assimilation with a global NICAM-LETKF system
P14 Yasumitsu Maejima
A Control simulation experiment for August 2014 severe rainfall event using a regional model
P15 Zhaoyang Huo
Four-dimensional relaxation ensemble Kalman filter in radar data assimilation

Poster size: A0 in portrait orientation (landscape is not allowed)



  • RIKEN Center for Computational Science (R-CCS) Data Assimilation Research Team
  • Email: da-ws-staff [at] ml.riken.jp (*Replace [at] with @)

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