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