文献类型：期刊文献

英文题名：Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks

作者：Cai, Pengjie[1];Huang, He[2];Liu, Taoli[1]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp Sci, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Sch Fisheries, Zhanjiang 524088, Peoples R China

年份：2024

卷号：24

期号：15

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001287020100001)、、EI(收录号：20243316867962)、Scopus(收录号：2-s2.0-85200880592)、WOS

基金：This work was funded by 1. Quality Engineering Project of Guangdong Ocean University: Industry-Education Integration Practice Teaching Base for Software Engineering Major of Guangdong Ocean University and China soft International (PX-129223525). 2. The key special project of the National Key Research and Development Program of the "135" Plan: "Research on the Technology System of Full-process Monitoring and Traceability of Cold Chain Logistics of Aquatic Products Based on Internet of Things Technology" (2019YFD0901605).

语种：英文

外文关键词：precipitation nowcasting; dual-polarization radar; generative adversarial network; deep learning

外文摘要：The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the "regression to average" issue of autoregressive model leads to the "blurring" phenomenon. The evolution method's generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method's generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the "regression to average" issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.