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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