Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

Abstract

AbstractWe present the first ever real‐time numerical weather prediction system with 30‐s update cycles at a 500‐m grid spacing for the prediction of convective precipitation in the subsequent 30 min using a new‐generation multi‐parameter phased array weather radar. The system comprises a regional atmospheric model known as the SCALE and the local ensemble transform Kalman filter (LETKF). To accelerate the SCALE‐LETKF system, data transfer between the two aforementioned components is performed using a memory copy instead of a file I/O. A complete real‐time workflow including domain nesting and observational data transfer is constructed. A real‐time test in July and August 2020 showed that the system is fast enough for a real‐time application of 30‐s forecast‐analysis cycles and 30‐min prediction. The development includes a new thinning method considering the spatially correlated observation errors in the dense radar data. This new thinning method is effective in two past case studies in the summer of 2019.