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

We 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.