Japan Geoscience Union Meeting 2016

Presentation information

International Session (Poster)

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

[A-AS02] High performance computing of next generation weather, climate, and environmental sciences using K

Sun. May 22, 2016 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall HALL6)

Convener:*Masaki Satoh(Atmosphere and Ocean Research Institute, The University of Tokyo), Masahide Kimoto(Atmosphere and Ocean Research Institute, The University of Tokyo), Kazuo Saito(Forecast Research Department, Meteorological Research Institute), Hiromu Seko(Meteorological Research Institute), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Tetsuro Tamura(Tokyo Institute of Technology), Hiroshi Niino(Dynamic Marine Meteorology Group, Department of Physical Oceanography, Atmosphere and Ocean Research Institute,The University of Tokyo), Masayuki Takigawa(Japan Agency for Marine-Earth Science and Technology), Hirofumi Tomita(AICS, RIKEN), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology)

5:15 PM - 6:30 PM

[AAS02-P03] High-resolution global atmospheric data assimilation experiments with an ensemble Kalman filter

*Koji Terasaki1, Shunji Kotsuki1, Takemasa Miyoshi1 (1.RIKEN Advanced Institute for Computational Science)

Keywords:Data assimilation, NICAM, Satellite Observations, AMSU-A

It is crucial to develop a numerical weather prediction system including data assimilation in order to predict the extreme weather such as heavy rainfalls and typhoons in the post-K era. We have been developing the NICAM-LETKF system to assimilate the conventional observations, satellite microwave radiances from AMSU-A (Advanced Microwave Sounding Unit-A), and satellite-based global precipitation data GSMaP (Global Satellite Mapping of Precipitation). The NICAM-LETKF may be run at very high resolution, or may provide boundary conditions for even higher resolution systems. Improving the NICAM-LETKF performance is at the center of enhancing mesoscale predictability for better preparedness for severe weather events well in advance.
Data assimilation experiments have been conducted with NICAM-LETKF at 112- and 28-km horizontal resolution with 100 ensemble members. Higher resolution experiment can reproduce the precipitation field well by assimilating precipitation observations. We need to keep improving the physical and computational performances of NICAM-LETKF to increase the resolution and the ensemble size, and to assimilate “Big Data” from the next-generation observations.