The Assimilation of Dual Phased Array Weather Radar Observations on Short-Range Convective Forecasts

The assimilation of Doppler velocity and reflectivity observations from phased array weather radar (PAWR) has been widely studied for the use of short-range numerical weather prediction (NWP) and has been found to have positive impact on analyses and forecasts. However, these studies only assimilated observations from a single PAWR and the use of multiple PAWR observations for NWP has not yet been explored. With the recent development of PAWR in Osaka and Kobe, Japan a common observation region exists where we are able to obtain dual PAWR observations in an area where convective storms can develop suddenly bringing intense rainfall and hazardous conditions.
In this study we present the first attempt at utilizing dual PAWR observations for the purpose of convective forecasts. We employ the use of the SCALE-LETKF system (Lien et al, 2017), which couples the Local Ensemble Transform Kalman Filter (LETKF) with the Scalable Computing for Advanced Library and Environment (SCALE)-Regional Model (RM), to perform simulations with 30-second-cycling of PAWR observations within a high-resolution mesh up to 250-m grid spacing. In the first part, we show how the assimilation of dual radar observations can improve short-range forecasts compared to the assimilation of a single radar observations. In the second part, we show how dual radar observations can be used to identify and remove false echoes, resulting in further improvements in convective forecasts.

GY Lien, T Miyoshi, S Nishizawa, R Yoshida, H Yashiro (2017): The near-real-time SCALE-LETKF system: A case of the September 2015 Kanto-Tohoku heavy rainfall. SOLA. Vol. 13, 1-6