30-second-Update 100-m-Mesh Data Assimilation Experiments: A Sudden Local Rain Case in Kobe on 11 September 2014

Abstract

This study aims to investigate the impacts of 30-second-update and 100-m-resolution data assimilation (DA) on a prediction of sudden local torrential rains caused by an isolated convective system in Kobe city on 11 September 2014. We perform a Local Ensemble Transform Kalman filter (LETKF) experiment with the Japan Meteorological Agency non-hydrostatic model (JMA-NHM) at 1-km and 100-m resolution using every-30-second radar reflectivity observed by the phased array weather radar (PAWR) at Osaka University. The 1-km-mesh experiment shows that 30-second-update PAWR DA has positive impacts on the analyses and forecasts. Moreover, the 100-m-mesh experiment shows significant advantages in representing the rainfall intensity and fine structure of the convective system. The promising results suggest that 30-second-update, 100-m-mesh DA have a great potential for predicting sudden local rain events.