[A-AS05] From Weather Predictability to Controllability

[AAS05-P01] Assimilation of Multi-Parameter Phased Array Radar Observations for High Precision Precipitation Forecasting using a 1000-member ensemble Kalman filter

*James David Taylor¹, Arata Amemiya¹, Shigenori Otsuka¹, Takemasa Miyoshi¹ (1.RIKEN Research center for computational science)

Keywords:data assimilation, phased array radar, convective scale forecasting

Multi-parameter phased array weather radar (MP-PAWR) is an advanced X-band radar system designed and built in Japan to provide high spatio-temporal resolution Doppler wind velocity and reflectivity observations for observing precipitating systems. Several studies have assimilated MP-PAWR observations for short-range numerical weather prediction (NWP), with positive impact to analyses and forecasts. This includes observations from the Saitama MP-PAWR, which were used as part of demonstration of the real-time SCALE-LETKF numerical modelling system to provide real-time forecasts of precipitation during the Tokyo Olympic and Paralympic Games in summer 2021. Recently, a MP-PAWR has been installed in Kobe, adding to MP-PAWR installed in Suita, Osaka, to provide observations of convective activity over Kansai. In this study we perform a series of data assimilation experiments to assimilate MP-PAWR observations from the newly installed Kobe MP-PAWR with the aim to improve short-range precipitation forecasting in the region. Experiments are performed with the SCALE-LETKF modelling system, using 1000-member ensemble with a 30-second update for a 500-m resolution mesh that covers a large area of the Kansai region. Strategies for assimilating dual-phased array radar observations for future demonstrations of the real-time system planned for 2024 and 2025 are also explored and demonstrated from preliminary experiments using Kobe and Osaka observations.