Observing System Simulation Experiments of a Rich Phased Array Weather Radar Network Covering Kyushu for the July 2020 Heavy Rainfall Event
Yasumitsu Maejima, Takuya Kawabata, Hiromu Seko, Takemasa Miyoshi
Abstract
This study investigates a potential impact of a rich phased array weather radar (PAWR) network covering Kyushu, Japan on numerical weather prediction (NWP) of the historic heavy rainfall event which caused a catastrophic disaster in southern Kumamoto on 4 July 2020. Perfect-model, identical-twin observing system simulation experiments (OSSEs) with 17 PAWRs are performed by the local ensemble transform Kalman filter (LETKF) with a regional NWP model known as the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM) at 1-km resolution. The nature run is generated by running the SCALE-RM initialized by the Japan Meteorological Agency (JMA) mesoscale model (MSM) analysis at 1800 JST 3 July 2020, showing sustained heavy rainfalls in southern Kumamoto on 4 July. Every 30-second synthetic reflectivity and radial winds are generated from the nature run at every model grid point below 20-km elevation within 60-km ranges from the 17 PAWRs. Two different control runs are generated, both failing to predict the heavy rainfalls in southern Kumamoto. In both cases, assimilating the PAWR data improves the heavy rainfall prediction mainly up to 1-hour lead time. The improvement decays gradually and is lost in about 3-hour lead time likely because the large-scale Baiu front dominates.