Data Assimilation Seminar – Professor Shu-Chih Yang

We held the 70th Data Assimilation Seminar online at RIKEN Center for Computational Science (R-CCS) on 1 October.

The talk was given by Professor Shu-Chih Yang from National Central University in Taiwan. She discussed her recent work with the NCU convective-scale ensemble data assimilation system and radar data assimilation research. Her updates included improvements in small-scale corrections, such as enhancement of convergence for convective development. Professor Yang also updated us on the work with ground based GNSS ZTD data with rapid update cycling.

We would like to thank Professor Yang once again for taking the time to present her recent updates with us and look forward to discussing about her research in future (hopefully in person!).

For more information about this and all our DA seminars at RIKEN R-CCS, please see the DA seminar page:

研究員もしくは上級研究員(無期雇用職)募集 (終了済み)







2020年12月8日 正午(日本時間)


Data Assimilation Seminar – Dr Guo-Yuan Lien

On 10 June we held the 67th DA seminar. The speaker was Dr Guo Yuan Lien from Central Weather Bureau in Taiwan.

Dr Lien spoke about the recent work with the operational global numerical weather prediction system used by the CWB including the assimilation of Global Navigation Satellite System radio occultation observations from the COSMIC-2 satellite as well as work with the EFSO on hybrid EnVar DA system.

Dr Lien is a former member of the DA team at RIKEN as it is always great to hear about his research and how his life back in native Taiwan is going. We look forward to hearing again from him in the near future.


Data Assimilation Seminar – Prof Shunji Kotsuki and Dr Keiichi Kondo

On September 11 2020 we held the 69th DA seminar online. The seminar was a joint presentation given by Prof. Shunji Kotsuki from Chiba University and Dr. Keiichi Kondo from JMA-MRI.

They spoke about their work using particle filters in use of high dimensional dynamical systems. Kotsuki-san talked about his development of a local particle filter with use with the LETKF data assimilation scheme and implementation of the Gaussian mixture to improve the local particle filter. His results showed that the added implementation of the Gaussian mixture outperforms the standard local particle filter for sparsely observed regions.

We would like to thank both Prof. Kotsuki and Dr Kondo for talking about their recent work and look forward to discussing more with them in future.



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