Events / Media

Data Assimilation Seminar Series

Dr. Shigenori Otsuka (Mar. 24, 2015, 15:30-)

Affiliation RIKEN-AICS
Title Towards 100-m resolution prediction of local convective storms: predictability and nowcasting
Abstract Recent developments in high-performance computing and advanced observing technologies such as the K computer and phased array weather radar (PAWR) enable us to step forward to cumulus-convective-scale data assimilation for numerical weather prediction (NWP) at a horizontal resolution of O(100) m, O(10) times higher than the previous studies. Understanding the predictability of convective-scale weather plays an essential role in designing such high-resolution NWP systems. In particular, it would be important to know what would be the effective temporal frequency of data assimilation, whether or not it needs to be the order of seconds. This study performs 30-second breeding cycles at a 100-m resolution and explores the convective-scale predictability. The results show that the bred vectors develop around the edge of newly developing convective cores and spread away from the convective cores. Taking advantage of rapid and dense observations by PAWR, we also performed short-term precipitation nowcasting experiments. Conventional nowcasting approaches are based on 2D five-minute-scan radar data and have difficulties in capturing a rapid development of convections. In this study, a 3D precipitation extrapolation system is developed based on the COTREC algorithm and tested using 3D PAWR volume scans at a 100-m resolution every 30 seconds with 100 vertical scan angles. The extrapolation system with the 3D PAWR data outperformed its 2D counterpart.
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