Japan Geoscience Union Meeting 2016

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International Session (Oral)

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 3:30 PM - 5:00 PM 302 (3F)

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), Chair:Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science)

4:30 PM - 4:45 PM

[AAS02-11] Non-Gaussian statics and data assimilation in the global atmospheric dynamics with 10240-member ensemble Kalman filter

★Invited papers

*KEIICHI KONDO1, Takemasa Miyoshi1 (1.RIKEN Advanced Institute for Computational Science)

Keywords:data assimilation, numerical weather prediction, non-Gaussianity

In our previous work, impacts of removing covariance localization are investigated by increasing the ensemble size up to 10240, with an intermediate AGCM known as the SPEEDY (T30/L7) model and an ensemble Kalman filter (EnKF). The analysis accuracy without localization was greatly improved, and we found that the long range covariance structures up to several thousand km helped to extract information from distant observations. By contrast, the improvement in the tropical regions was relatively small. In this study, we hypothesize that this little improvements be related to the non-Gaussianity of the error statistics due to highly-nonlinear processes of convections. Actually, we found that strong non-Gaussianity such as bimodal distributions frequently appears in the tropical regions, and the spatial patterns of the occurrences of the non-Gaussian error statistics correspond well to that of the analysis error. We test some ideas to partly account for non-Gaussianity in the EnKF framework. We will present the results up to the time of the workshop.