Assimilation of Phased-Array Weather Radar Observations in a Convective Precipitation Event Using a 50 m Grid-Spacing NWP Model

Rapidly developing convective clouds have a small scale of several kilometers or less and often have a significant impact on society. The evolution of such clouds can be observed by state-of-the-art phased-array weather radars (PAWRs). This study aims to assimilate high spatiotemporal resolution observations from a PAWR installed in Japan to predict the rapid development of convective clouds. To do so, this study employs an ensemble Kalman filter (EnKF) data assimilation system with a high-resolution numerical weather prediction (NWP) model, which uses the horizontal grid spacing of 50 m. Such a high resolution would be suitable to capture the rapid growth of small-scale forecast errors. Preliminary results show that the assimilation of PAWR observations successfully improves the distribution of precipitation systems.