日本地球惑星科学連合2016年大会

講演情報

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22日(日) 15:30 〜 17:00 302 (3F)

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:三好 建正(理化学研究所計算科学研究機構)

16:30 〜 16:45

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

★招待講演

*近藤 圭一1三好 建正1 (1.国立研究開発法人 理化学研究所 計算科学研究機構)

キーワード: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.