Japan Geoscience Union Meeting 2019

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS01] High performance computing for next generation weather, climate, and environmental sc iences

Wed. May 29, 2019 9:00 AM - 10:30 AM 104 (1F)

convener:Hiromu Seko(Meteorological Research Institute), Takemasa Miyoshi(RIKEN), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology), Masayuki Takigawa(Japan Agency for Marine-Earth Science and Technology), Chairperson:Hiromu Seko(Meteorological Research Institute), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology)

9:00 AM - 9:15 AM

[AAS01-01] Data Assimilation Studies using Big Observation Data in the Projects of Post K and BDA

*Hiromu Seko1, Masaru Kunii2, Yohei Sawada1, Kozo Okamoto1, Sho Yokota1, Kosuke Ito3, Kazuki Shimoji2 (1.Meteorological Research Institute, 2.Japan Meteorological Agency, 3.University of the Ryukyus)

Keywords:Data Assimilation, Big Observation Data

In the projects of 'Advancement of meteorological and global environmental predictions utilizing observational Big Data' of 'The post K computer development plan of the FLAGSHIP2020 Project' and 'Innovating "Big Data Assimilation" Technology for Revolutionizing Very-short-range Severe Weather Prediction', the data assimilation techniques including the coupling with the ocean model have been developed to improve the prediction accuracy of heavy rainfalls, typhoons and tornadoes, and to obtain the longer leading time (the time from the prediction to the occurrences of severe phenomena). The most of these studies were conducted by using the super computer 'K' and Big Observation Data. In this presentation, the impacts of rapid scan'atmospheric motion vector' and sea surface temperature of Himawari-8 on the Typhoon intensity and track predictions and on the rainfall predictions of heavy rainfalls, the assimilation results of all-sky radiance data in the convection scale prediction, and the reproduction of rainfall system that caused tornadoes by the data of Polarization radar and dense surface observation network will be presented.