中国海洋大学学报（自然科学版）2024，Vol.54Issue(2)：23-32,10.DOI:10.16441/j.cnki.hdxb.20220350
基于分层生成对抗网络的短临降水预报方法研究
Research on Precipitation Nowcasting Based on Hierarchical Generative Adversarial Network
摘要
Abstract
This paper attempts to improve the accuracy of precipitation nowcasting by using generative adversarial network(GAN)method in deep learning.The hierarchical generative adversarial network(HGAN)is proposed to generate future radar echo sequences based on historical radar echo sequences.HGAN is composed of a global generator and a local discriminator.The global generator is constructed in a hierarchical structure of multiple subnets,and the model is trained using an upsampling process to capture the evolution trend of radar echoes,which is conducive to generating a clear future radar echo map.The local discriminator distinguishes the predicted radar echo map from the observed radar echo map based on the local area,and introduces a buffer mechanism to save the historical prediction se-quence so that the final prediction is more time-series compliant.Both are trained in an adversarial man-ner,and the resulting model is able to generate sufficiently clear and close to realistic future radar echo sequences for more accurate portrayal of echo intensity extremes and ranges.The test set verification and individual case analysis are performed for HGAN and GAN,and the experimental results verify the effectiveness of HGAN for radar echo prediction.Meanwhile,the critical success index and probability of detection of HGAN are higher than those of GAN,while the false alarm rate is lower than that of GAN under the same test reflectivity threshold,and the structural similarity index(SSIM)of HGAN is better than that of GAN under the same prediction duration.关键词
短临降水/雷达回波/分层生成对抗网络/全局生成器/局部鉴别器Key words
precipitation nowcasting/radar echo/hierarchical generative adversarial/global genera-tor/local discriminator分类
天文与地球科学引用本文复制引用
曾强胜,郭敬天,任鹏,黄文华,王宁..基于分层生成对抗网络的短临降水预报方法研究[J].中国海洋大学学报（自然科学版）,2024,54(2):23-32,10.基金项目
国家重点研究发展计划项目(2018YFC1407002)资助Supported by the National Key Research and Development Program of China(2018YFC1407002) （2018YFC1407002）
