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

Journal of Advances in Modeling Earth Systems

Y. Ishikawa¹  S. Satoh²  A. Amemiya³  T. Yamaura³  G.‐Y. Lien³  T. Miyoshi³  J. Taylor³  S. Otsuka³  Y. Maejima³  K. Sueki³  T. Honda³  S. Nishizawa³  H. Tomita³ 

[1] National Institute of Informatics Chiyoda‐ku Japan;National Institute of Information and Communications Technology Koganei Japan;RIKEN Center for Computational Science Kobe Japan;

关键词: data assimilation;    phased‐array weather radar;    numerical weather prediction;   

DOI  :  10.1029/2021MS002823

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown