Write For Us

Predict Sudden Severe Rainstorm with Data Assimilation : Takemasa Miyoshi at TEDxSannomiya

E-Commerce Solutions SEO Solutions Marketing Solutions
316 İzlenme
Published
Takemasa Miyoshi - Data Assimilation Researcher

Takemasa Miyoshi was born in Aomori in 1977 and grew up in Kanagawa, Japan. In 2000, Miyoshi graduated from the Science Faculty of Kyoto University, and he started at the Japanese Meteorological Agency (JMA). In 2003, he received a Japanese government fellowship to study at the University of Maryland (UMD), and completed both M.S. and Ph.D. degrees on ensemble data assimilation within two years. Miyoshi moved back to JMA in 2005, and he worked as the Scientific Official of the Numerical Prediction Division. In 2008, he received the Yamamoto-Shono Award from the Japanese Meteorological Society. He moved to UMD as a Research Assistant Professor in 2009. Miyoshi has been the Visiting Professor of the department of Atmospheric and Oceanic Science, University of Maryland since January 2013. Also, he has been the Senior Research Scientist of the Earth Simulator Center.

He mainly researches a weather forecast with a supercomputer. Data Assimilation is like a bridge between numerical simulation models and actual observations, and it has great impact on weather forecast. Miyoshi has been working towards his goals of improving the accuracy of weather forecast with Data Assimilation.

[FYI]

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Kategori
Eğitim
Yorum yazmak için Giriş yap ya da Üye ol .
Henüz yorum yapılmamış. İlk yorumu siz yapın.