부산대학교 도서관

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. Plain Language Summary We developed the first ever real-time weather prediction system that refreshes forecasts every 30 s using frequent observations for the forecasting of sudden heavy rainfall, a major risk in the warm season. The system takes advantage of state-of-the-art radar observations and performs predictions for the succeeding 30 min at 500-m horizontal grid spacing. A real-time test in July and August 2020 showed that the system is fast enough to update forecasts every 30 s in real time. We evaluated the system using past case data and found that it accurately predicted small-scale rainfalls. [ABSTRACT FROM AUTHOR]