日本地球惑星科学連合2019年大会

講演情報

[E] ポスター発表

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-AS01] 高性能スーパーコンピュータを用いた最新の大気科学

2019年5月29日(水) 15:30 〜 17:00 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:瀬古 弘(気象研究所)、三好 建正(理化学研究所)、小玉 知央(独立行政法人海洋研究開発機構)、滝川 雅之(独立行政法人海洋研究開発機構)

[AAS01-P05] Assimilating every 30-second phased array weather radar data in a torrential rainfall event on July 6, 2018 around Kobe city

*前島 康光1大塚 成徳1三好 建正1,2 (1.理化学研究所 計算科学研究センター、2.メリーランド大学 カレッジパーク)

キーワード:データ同化、豪雨予報、高性能計算

To investigate the impact of every 30-second phased array weather radar (PAWR; Yoshikawa et al. 2013, Ushio et al. 2014) observation on a simulation of a severe rainfall event occurred on July 6, 2018 around Kobe city, we perform 30-second-update 100-m-mesh data assimilation (DA) experiments using the Local Ensemble Transform Kalman Filter with the Scalable Computing for Advanced Library and Environment regional numerical weather prediction model. Two experiments were performed: the test experiment with every 30-second PAWR observation (TEST), and the other without observation (NO-DA).

The TEST analysis shows intense rainfalls with detailed structure of active convection, better matching with the PAWR observation compared to NO-DA analysis. In the forecast experiment, the forecast initialized by the ensemble mean analysis of TEST is skillful for 20 minutes compared with NO-DA, although the skill is decreased rapidly. The results suggest that the PAWR DA have a potential to improve the numerical simulation for this torrential rainfall event.