Precipitation nowcasting with a new three-dimensional super-rapid phased array weather radar

SHIGENORI OTSUKA; GULAMBAER TUERHONG; JUAN RUIZ; R. KIKUCHI; Y. KITANO; Y. TANIGUCHI; TAKEMASA MIYOSHI

Kobe

Simposio; The 4th International Symposium on Data Assimilation, Kobe; 2015

RIKEN Advanced Center for Computational Science

Short-range precipitation forecasting (a.k.a. nowcasting) for the next 10 minutes to an hour plays an important role in the last-minute preparation for meteorological disasters such as flash-floods and landslides. In this study, a new nowcasting system is developed using the latest phased array weather radar (PAWR) which is capable of a 100-m resolution 3-D volume scan every 30 seconds for 100 vertical scan levels. PAWR produces ~100 times more data than the conventional parabolic-antenna radar with a volume scan typically made every 5 minutes for 15 scan levels. A new nowcasting algorithm needs to be developed to take advantage of the dense and frequent PAWR. First, motion vectors are estimated based on the COTREC algorithm using two consecutive volume scans. Second, the precipitation field is extrapolated with the motion vectors based on a weighted essentially non-oscillatory (WENO) scheme. We applied the nowcasting system to a synthetic dataset produced by a numerical simulation using the Weather Research and Forecasting (WRF) model to investigate the performance in an idealized situation. Next, we applied the algorithm to the noisy real PAWR observations. The extrapolation system showed improved forecasting results with the dense and frequent PAWR data compared to its 2D counterparts.