日本地球惑星科学連合2016年大会

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インターナショナルセッション(口頭発表)

セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

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

2016年5月23日(月) 09:00 〜 10:30 102 (1F)

コンビーナ:*佐藤 正樹(東京大学大気海洋研究所)、木本 昌秀(東京大学大気海洋研究所)、斉藤 和雄(気象研究所予報研究部)、瀬古 弘(気象研究所)、三好 建正(理化学研究所計算科学研究機構)、田村 哲郎(東京工業大学大学院総合理工学研究科)、新野 宏(東京大学大気海洋研究所海洋物理学部門海洋大気力学分野)、滝川 雅之(独立行政法人海洋研究開発機構)、富田 浩文(理化学研究所計算科学研究機構)、小玉 知央(独立行政法人海洋研究開発機構)、座長:滝川 雅之(独立行政法人海洋研究開発機構)

10:00 〜 10:15

[AAS02-17] Month-long forecasts using a global non-hydrostatic model in boreal summer season

*那須野 智江1中野 満寿男1沢田 雅洋2 (1.国立研究開発法人 海洋研究開発機構、2.気象研究所)

キーワード:Global nonhydrostatic model, Tropical Cyclogenesis, Boreal Summer Intraseasonal Oscillation

Month-long forecasts using a global non-hydrostatic model (Nonhydrostatic Icosahedral Atmospheric Model, NICAM) have been routinely run (once a week) during boreal summer season in 2014 and 2015. The model was initialized using NCEP final analysis and free run was conducted with prescribed sea surface temperature. Horizontal mesh size of 14-km was globally used with explicit representation of moist convection. In both years, El Nino was developing and successive formation of tropical cyclones took place in the western North Pacific during the active periods of intraseasonal oscillation (ISO). The model generally captured the large-scale variability associated with the ISO, such as the eastward and northward extension of lower tropospheric equatorial westerlies and convective activity at the lead time of approximately two weeks. These results support the arguments of previous studies based on NICAM ensemble simulations using the K-computer. Some common biases were noted, such as northward displacement of monsoonal circulation and earlier growth of convectively coupled vorticity disturbances. By fixing these biases, extension of predictability is highly expected. The simulation results also suggest that better prediction of major convective systems, such as tropical cyclones, leads to better forecast skills in large-scale fields.